TECHNICAL SUPPORT DOCUMENT:

ENERGY EFFICIENCY PROGRAM

FOR CONSUMER PRODUCTS AND 

COMMERCIAL AND INDUSTRIAL EQUIPMENT:

AUTOMATIC COMMERCIAL ICE MAKERS

December 1, 2014

U.S. Department of Energy

Assistant Secretary

Office of Energy Efficiency and Renewable Energy

Building Technologies Program

Appliances and Commercial Equipment Standards

Washington, DC 20585
This document was prepared for the Department of Energy

by staff members of

Navigant Consulting, Inc.

and

Pacific Northwest National Laboratory
CHAPTER 1. INTRODUCTION

TABLE OF CONTENTS

1.1 PURPOSE OF THE DOCUMENT ................................................................................. 1-1

1.2 SUMMARY OF NATIONAL BENEFITS ..................................................................... 1-1

1.3 OVERVIEW OF APPLIANCE STANDARDS .............................................................. 1-4

1.4 OVERVIEW OF AUTOMATIC COMMERCIAL ICE MAKERS STANDARDS....... 1-5

1.4.1 Framework and Analysis Methodology............................................................... 1-6

1.5 STRUCTURE OF THE DOCUMENT............................................................................ 1-9

REFERENCES .......................................................................................................................... 1-12

LIST OF TABLES

Table 1.2.1 Annualized Benefits and Costs of Proposed Standards for Automatic 

Commercial Ice Makers....................................................................................... 1-3

Table 1.4.1 ACIM Standards Prescribed by EPCA – Compliance Required Beginning 

on January 1, 2010 ............................................................................................... 1-5

Table 1.4.2 ACIM Analyses ........................................................................................................ 1-7

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CHAPTER 1. INTRODUCTION

1.1 PURPOSE OF THE DOCUMENT

This technical support document (TSD) is a stand-alone report that provides the technical 

analyses and results in support of the information presented in the final rule for automatic 

commercial ice makers completed in December 2014. This final rule TSD complements the lifecycle cost (LCC), payback period (PBP), and national impact analysis (NIA) spreadsheets posted 

on the U.S. Department of Energy’s (DOE’s) website at

www1.eere.energy.gov/buildings/appliance_standards/rulemaking.aspx/ruleid/29.

1.2 SUMMARY OF NATIONAL BENEFITS

DOE’s analyses indicate that the proposed standards would save a significant amount of 

energy. The lifetime savings for automatic commercial ice-making (ACIM) equipment

purchased in the 30-year period that begins in the year of the compliance with amended 

standards (2018–2047) amount to 0.179 quadrillion British thermal units (quads) of cumulative 

energy.

The cumulative national net present value (NPV) of total customer costs and savings of 

the proposed standards for automatic commercial ice makers in 2013$ ranges from $0.430 billion 

(at a 7-percent discount rate) to $0.942 billion (at a 3-percent discount rate). This NPV expresses 

the estimated total value of future operating cost savings minus the estimated increased installed 

costs for equipment purchased in 2018–2047, discounted to 2014.

DOE also studied the potential impact on the ACIM equipment manufacturers. The 

industry net present value (INPV) is the sum of the discounted cash flows to the industry from 

the base year (2015) through the end of the analysis period (2047). Using a real discount rate of 

9.2 percent,a DOE estimates that the INPV of ACIM manufacturers is $121.6 million in the base 

case in 2013$. Under the proposed standards, DOE expects that manufacturers may lose up to 

12.5 percent of their INPV, or approximately $15.1 million. 

The proposed standards are expected to have significant environmental benefits. Based 

on energy savings from equipment purchased over the period of 2018–2047, the energy savings 

would result in cumulative greenhouse gas emission reductions of 10.9 million metric tonsb of 

carbon dioxide (CO2), 47.4 thousand tons of methane, 0.1 thousand tons of nitrous oxide, 

9.3 thousand tons of sulfur dioxide (SO2), 16.2 thousand tons of nitrogen oxides (NOx), and 

0.03 tons of mercury (Hg).

c DOE estimates that the net present monetary value of the CO2 

emissions reduction would be between $0.08 and $1.11 billion, expressed in 2013$ and 

discounted to 2014. DOE estimates the present monetary value of the NOx emissions reduction 

a This is the rate used to discount future cash flows in the manufacturer impact analysis. A discount rate of 10 

percent was calculated based on SEC filings and feedback from manufacturer interviews about the current cost of 

capital in the industry. For more information, refer to chapter 12 of the NOPR TSD.

b A metric ton is equivalent to 1.1 U.S. short tons. Results for NOx and Hg are presented in short tons. c DOE calculated emissions reductions relative to the Annual Energy Outlook 2014 (AEO2014) Reference Case, 

which generally represents current legislation and environmental regulations for which implementing regulations 

were available as of October 31, 2013.

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would be $12.1 million at a 7-percent discount rate and $23.8 million at a 3-percent discount 

rate.d

The benefits and costs of the proposed standards, for automatic commercial ice makers

purchased between 2018 and 2047, can also be expressed in terms of annualized values. The 

annualized monetary values are the sum of (1) the annualized national economic value of the 

benefits from the customer operation of equipment that meets the proposed standards (consisting 

primarily of operating cost savings from using less energy, minus increases in equipment 

installed cost, which is another way of representing customer NPV) and (2) the annualized 

monetary value of the benefits of emission reductions, including CO2 emission reductions.e

The value of the CO2 reductions is calculated using a range of values per metric ton of CO2

(otherwise known as the Social Cost of Carbon [SCC]) developed by a recent Federal 

interagency process. The monetary costs and benefits of cumulative emissions reductions are 

reported in 2013$ to permit straightforward comparisons with the other costs and benefits. The 

derivation of the values of the SCC is discussed in appendices 14A and 14B. 

Although combining the values of operating savings and CO2 emission reductions 

provides a useful perspective, two issues should be considered. First, the national operating 

savings are domestic U.S. customer monetary savings that occur as a result of market 

transactions, while the value of CO2 reductions is based on a global value. Second, the 

assessments of operating cost savings and CO2 savings are performed with different methods that 

use different time frames for analysis. The national operating cost savings are measured over the 

lifetimes of automatic commercial ice makers shipped in 2018–2047. The SCC values, on the 

other hand, reflect the present value of some future climate-related impacts resulting from the 

emission of 1 ton of CO2 in each year. These impacts continue well beyond 2100.

Table 1.2.1 shows the annualized benefits and costs of the proposed standards. The 

results of the primary estimate are as follows. Table 1.2.1 shows the primary, low net benefits,

and high net benefits scenarios. The primary estimate is the estimate in which the operating cost 

savings were calculated using the Annual Energy Outlook 2014 (AEO2014)

1 Reference Case 

forecast of future electricity prices. The other two estimates, low net benefits and high net 

benefits estimates, are based on the low and high electricity price scenarios from the AEO2014

forecast. At a 7-percent discount rate for benefits and costs, the cost in the primary estimate of 

the standards proposed in this final rule is $22 million per year in increased equipment costs. The 

annualized benefits are $65 million per year in reduced equipment operating costs, $20 million in 

CO2 reductions (note that DOE used a 3-percent discount rate, along with the corresponding 

SCC series that uses a 3-percent discount rate, to calculate the monetized value of CO2 emissions 

reductions), and $1.2 million in reduced NOx emissions. In this case, the annualized net benefit 

d DOE is currently investigating valuation of avoided Hg and SO2 emissions. e DOE used a two-step calculation process to convert the time-series of costs and benefits into annualized values. 

First, DOE calculated a present value in 2014, the year used for discounting the NPV of total consumer costs and 

savings, for the time-series of costs and benefits using discount rates of 3 and 7 percent for all costs and benefits 

except for the value of CO2 reductions. For the latter, DOE used a range of discount rates, as shown in Table 1.2.1. 

From the present value, DOE then calculated the fixed annual payment over a 30-year period (2018 through 2047) 

that yields the same present value. The fixed annual payment is the annualized value. Although DOE calculated 

annualized values, this does not imply that the time-series of cost and benefits from which the annualized values 

were determined is a steady stream of payments.

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amounts to $64 million. At a 3-percent discount rate for all benefits and costs, the cost in the 

primary estimate of the amended standards proposed in this final rule is $23 million per year in 

increased equipment costs. The benefits are $75 million per year in reduced operating costs, $20

million in CO2 reductions, and $1.3 million in reduced NOx emissions. In this case, the net 

benefit amounts to $74 million per year. 

Table 1.2.1 Annualized Benefits and Costs of Proposed Standards for Automatic 

Commercial Ice Makers

Discount 

Rate

Primary 

Estimate*

million 2013$

Low Net 

Benefits 

Estimate*

million 2013$

High Net 

Benefits 

Estimate*

million 2013$

Benefits

Operating Cost Savings 7% 65 62 68

3% 75 71 80

CO2 at 5% discount rate, average** 5% 6 6 6

CO2 at 3% discount rate, average** 3% 20 20 21

CO2 at 2.5% discount rate, average** 2.5% 29 28 30

CO2 at 3% discount rate, 95th 

percentile** 3% 62 60 64

NOx Reduction Monetized Value (at 

$2,684/Ton)**

7% 1.19 1.16 1.22

3% 1.33 1.29 1.36

Total Benefits (Operating Cost 

Savings, CO2 Reduction and NOx

Reduction)†

7% 86 82 90

3% 97 92 102

Costs

Total Incremental Installed Costs 7% 22 23 21

3% 23 24 22

Net Benefits Less Costs

Total Benefits Less Incremental Costs 7% 64 60 69

3% 74 68 80

* The primary, low, and high estimates utilize forecasts of energy prices from the AEO2014 Reference Case, Low 

Economic Growth Case, and High Economic Growth Case, respectively.

** These values represent global values (in 2013$) of the social cost of CO2 emissions in 2015 under several 

scenarios. The values of $12.0, $40.5, and $62.4 per ton are the averages of SCC distributions calculated using 

5-percent, 3-percent, and 2.5-percent discount rates, respectively. The value of $119.0 per ton represents the 95th 

percentile of the SCC distribution calculated using a 3-percent discount rate. See TSD chapter 8 for details. For NOx, 

an average value ($2,684) of the low ($476) and high ($4,893) values was used.

† Total monetary benefits for both the 3-percent and 7-percent cases utilize the central estimate of social cost of NOx

and CO2 emissions calculated at a 3-percent discount rate (averaged across three integrated assessment models) , 

which is equal to $40.5/ton (in 2013$).

DOE also estimated the low net benefits and high net benefits by calculating the 

operating cost savings and shipments at the AEO2014 low economic growth case and high 

economic growth case scenarios, respectively. The low and high benefits for incremental 

installed costs were derived using the low and high price learning scenarios. The net benefits and 

costs for low and high net benefits estimates were calculated in the same manner as the primary 

estimate by using the corresponding values of operating cost savings and incremental installed 

costs. 

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1.3 OVERVIEW OF APPLIANCE STANDARDS

Part B of Title III of the Energy Policy and Conservation Act of 1975 (EPCA or the Act), 

Pub. L. 94-163, as amended by the National Energy Conservation Policy Act of 1978 (NECPA), 

Pub. L. 95-619; the National Appliance Energy Conservation Act of 1987 (NAECA), Pub. L.

100-12; the National Appliance Energy Conservation Amendments of 1988 (NAECA 1988), 

Pub. L. 100-357; and the Energy Policy Act of 1992 (EPACT 1992), Pub. L. 102-486, 

established the Energy Conservation Program for Consumer Products other than Automobiles. 

(42 U.S.C. 6291–6309) Part 3 of Title IV of NECPA amended EPCA to add Part A-1 of Title III, 

which established an energy conservation program for certain industrial equipment.

f (42 U.S.C. 

6311–6317) EPACT 1992 included amendments to EPCA that expanded Title III to include 

additional commercial equipment. The Energy Policy and Conservation Act of 2005 

(EPACT 2005), Pub. L. 109-58, updated several existing standards and test procedures; 

prescribed definitions, standards, and test procedures for certain new consumer products and 

commercial equipment; and mandated that the Secretary of Energy (the Secretary) commence 

rulemakings to develop test procedures and standards for certain new consumer products and 

commercial equipment including ACIM equipment.

DOE is required to design each standard for this equipment to (1) achieve the maximum 

improvement in energy efficiency that is technologically feasible and economically justified and 

(2) result in significant conservation of energy. (42 U.S.C. 6295(o)(2)(A) and (o)(3)(B), 

42 U.S.C. 6313(d)(4)) To determine whether a proposed standard is economically justified, DOE 

will, after receiving comments on the proposed standard, determine whether the benefits of the 

standard exceed its burdens to the greatest extent practicable, considering the following seven 

factors:

1. the economic impact of the standard on the manufacturers and on the consumers of 

the products subject to such standard;

2. the savings in operating costs throughout the estimated average life of the covered 

product in the type (or class) compared to any increase in the price of, or in the initial 

charges for maintenance expenses of, the covered products that are likely to result 

from the imposition of the standard; 

3. the total projected amount of energy savings likely to result directly from the 

imposition of the standard; 

4. any lessening of the utility or the performance of the covered products likely to result 

from the imposition of the standard; 

5. the impact of any lessening of competition, as determined in writing by the Attorney 

General, that is likely to result from the imposition of the standard; 

6. the need for national energy conservation; and

f This part was originally titled Part C. However, it was redesignated Part A-1 after Part B of Title III of EPCA was 

repealed by Pub. L. 109-58.

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7. other factors the Secretary considers relevant.

(See 42 U.S.C. 6295(o)(2)(B)(i); 6313(d)(4))

For automatic commercial ice makers, DOE applied those factors in a manner consistent 

with its other energy conservation standards rulemakings to ascertain the maximum 

improvement in energy efficiency that is technologically feasible and economically justified for 

this equipment.

1.4 OVERVIEW OF AUTOMATIC COMMERCIAL ICE MAKERS STANDARDS

EPCA prescribed energy conservation standards for automatic commercial ice makers 

that produce cube type ice with capacities between 50 and 2,500 lb ice/24 hours. (42 U.S.C. 

6313(d)(1)) In a final rule published on October 18, 2005, DOE adopted the energy conservation 

standards and water conservation standards prescribed by EPCA in 42 U.S.C. 6313(d)(1) for 

certain automatic commercial ice makers manufactured on or after January 1, 2010. 70 FR at 

60407, 60415–16 . These standards consist of maximum energy use and maximum condenser 

water use to produce 100 lb of ice for automatic commercial ice makers with harvest rates 

between 50 and 2,500 lb ice/24 hours. These standards appear at Title 10 of the Code of Federal 

Regulations (CFR) part 431, subpart H, Automatic Commercial Ice Makers. Table 1.4.1 presents 

DOE’s current energy conservation standards for automatic commercial ice makers.

Table 1.4.1 ACIM Standards Prescribed by EPCA – Compliance Required Beginning on 

January 1, 2010

Equipment Type Type of 

Cooling

Harvest Rate

lb ice/24 hours

Maximum Energy 

Use

kWh/100 lb ice

Maximum Condenser 

Water Use*

gal/100 lb ice

Ice-Making Head

Water

<500 7.8-0.0055H** 200-0.022H**

≥500 and <1,436 5.58-0.0011H 200-0.022H

≥1,436 4.0 200-0.022H

Air <450 10.26-0.0086H Not Applicable

≥450 6.89-0.0011H Not Applicable

Remote Condensing 

(but not remote 

compressor)

Air

<1,000 8.85-0.0038H Not Applicable

≥1,000 5.10 Not Applicable

Remote Condensing 

and Remote 

Compressor

Air

<934 8.85-0.0038H Not Applicable

≥934 5.30 Not Applicable

Self-Contained

Water <200 11.4-0.019H 191-0.0315H

≥200 7.60 191-0.0315H

Air <175 18.0-0.0469H Not Applicable

≥175 9.80 Not Applicable

Source: 42 U.S.C. 6313(d).

* Water use is for the condenser only and does not include potable water used to make ice.

** H = harvest rate in pounds per 24 hours, indicating the water or energy use for a given harvest rate.

Furthermore, EPCA requires DOE to review these standards and determine, by January 1, 

2015, whether amending the applicable standards is technically feasible and economically 

justified. (42 U.S.C. 6313(d)(3)(A)) If amended standards are technically feasible and 

economically justified, DOE must issue a final rule by the same date. (42 U.S.C. 6313(d)(3)(B)) 

Additionally, EPCA granted DOE the authority to conduct rulemakings to establish new 

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standards for automatic commercial ice makers not covered by 42 U.S.C. 6313(d)(1)). (42 U.S.C. 

6313(d)(2)(A)) DOE initiated this rulemaking for ACIM equipment to satisfy EPCA’s 

requirement. 

1.4.1 Framework and Analysis Methodology

DOE initiated this rulemaking to fulfill its statutory requirements set forth in 42 U.S.C. 

6313(d) with respect to establishing amended energy conservation standards for automatic 

commercial ice makers. As the first step, on November 19, 2010, DOE published a Rulemaking 

Framework for Automatic Commercial Ice Makers describing the procedural and analytical

approaches DOE anticipated using to evaluate the establishment of energy conservation 

standards for automatic commercial ice makers. This document is available at

www1.eere.energy.gov/buildings/appliance_standards/rulemaking.aspx/ruleid/29. 

DOE held a public meeting on December 16, 2010 (December 2010 framework public 

meeting) to discuss procedural and analytical approaches to the rulemaking and to inform 

interested parties and facilitate their involvement in the rulemaking process. The analytical 

framework presented at the public meeting described different analyses, such as the engineering 

analysis and the LCC and PBP analyses, the methods proposed for conducting them, and the 

relationships among the various analyses.

After the December 2010 framework public meeting, as part of the information gathering 

and sharing process for the preliminary manufacturer impact analysis (MIA), DOE organized 

and held interviews with ACIM manufacturers. DOE selected companies that represented 

production of all types of automatic commercial ice makers, ranging from small to large 

manufacturers. DOE had four objectives for these interviews: (1) solicit feedback on the draft 

engineering analysis (including methodology, production costs, manufacturing processes, and 

findings); (2) solicit feedback on topics related to the preliminary MIA; (3) provide an 

opportunity, early in the rulemaking process, for manufacturers to express specific concerns to 

DOE; and (4) foster cooperation between the manufacturers and DOE.

In January 2012, DOE published a notice of public meeting and availability of the 

preliminary TSD (January 2012 preliminary analysis) for the ongoing rulemaking to potentially

amend energy conservation standards for automatic commercial ice makers. 77 FR 3404 (Jan. 

24, 2012).

DOE held a public meeting on February 16, 2012 (February 2012 preliminary analysis 

public meeting) to give stakeholders the opportunity to comment on the proposed equipment 

classes DOE is considering; the analytical framework, models, and tools (e.g., LCC and NIA

spreadsheets) that DOE has been using to perform analyses of the impacts of energy 

conservation standards; the results of the preliminary analyses; and the candidate energy 

conservation standard levels.

See Table 1.4.2 for all the analyses discussed at the public meeting and where the 

analyses are addressed in each of the formal public rulemaking documents.

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Table 1.4.2 ACIM Analyses 

Preliminary Analysis Notice of Proposed Rulemaking Final Rule*

Market and technology assessment Revised preliminary analyses Revised NOPR analyses

Screening analysis Customer subgroup analysis

Engineering analysis MIA

Energy use characterization Emissions analysis

Markups to determine equipment price Emissions monetization

LCC and PBP analyses Employment impact analysis

Shipments analysis Utility impact analysis

NIA Regulatory impact analysis

Preliminary MIA

* During the final rule phase, DOE considers the comments submitted by the U.S. Department of Justice in the NOPR phase 

concerning the impact of any lessening of competition that is likely to result from the imposition of the standard. (42 U.S.C. 

6295(o)(2)(B)(v))

DOE developed the spreadsheets for the LCC and PBP analyses and for the NIA to meet 

the objectives of the July 15, 1996, Procedures for Consideration of New or Revised Energy 

Conservation Standards for Consumer Products (the “Process Rule”). 61 FR 36974 (July 15, 

1996). The LCC spreadsheet calculates national distributions of LCC savings at all efficiency 

levels above the baseline. The NIA spreadsheet calculates the national energy savings and 

national NPVs at all efficiency levels, and includes a module that forecasts shipments for the 

various equipment classes of automatic commercial ice makers at different efficiency levels.

After the posting of the January 2012 preliminary analysis TSD and the February 2012 

preliminary analysis public meeting, DOE revised the analyses pursuant to comments and input 

received in response to the preliminary analysis. In addition to revising the various preliminary 

analyses, DOE also performed a customer subgroup analysis, manufacturer impact analysis, 

emissions analysis and monetization, utility impact analysis, employment impact analysis, and 

regulatory impact analysis for the Notice of Proposed Rulemaking (NOPR). DOE also conducted 

interviews with ACIM manufacturers as part of the MIA for the NOPR. The results of these 

interviews were incorporated into the final rule engineering analysis (TSD chapter 5) and MIA 

(TSD chapter 12).

On March 17, 2014, DOE published a NOPR in the Federal Register (March 2014 

NOPR). 79 FR 14846. In the March 2014 NOPR, DOE addressed, in detail, the comments 

received in earlier stages of rulemaking, and proposed amended energy conservation standards 

for automatic commercial ice makers. In conjunction with the March 2014 NOPR, DOE also 

published on its website the complete TSD for the proposed rule, which incorporated the 

analyses DOE conducted and technical documentation for each analysis. Also published on 

DOE’s website were the engineering analysis spreadsheets, the LCC spreadsheet, and the NIA

spreadsheet. These materials are available at 

http://www1.eere.energy.gov/buildings/appliance_standards/rulemaking.aspx/ruleid/29

In the March 2014 NOPR, DOE identified 19 issues for which it was particularly

interested in receiving comments and views of interested parties: standards compliance dates, 

utilization factors, baseline efficiency, screening analysis, maximum technology feasibility, 

markups, equipment life, installation costs, open- vs. closed-loop installations, ice maker 

shipments by type of equipment, intermittency of manufacturer research and development 

(R&D) and impact of standards, INPV results and impact of standards, small businesses, 

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consumer utility and performance, analysis period, social cost of carbon, remote to rack 

equipment, design options associated with each TSD, and standard levels for batch type ice 

makers over 2,500 lb ice/hour. 79 FR 14846 at 14947-49. 

After the publication of the March 2014 NOPR, DOE received written comments on 

these and other issues. DOE also held a public meeting in Washington, DC, on April 14, 2014, to 

discuss and receive comments regarding the tools and methods DOE used in the NOPR analysis, 

as well as the results of the analysis. DOE also invited written comments and announced the 

availability of a NOPR analysis technical support document (NOPR TSD). The NOPR TSD is 

available at http://www.regulations.gov/#!documentDetail;D=EERE-2010-BT-STD-0037-0061 

At the public meeting held on April 14, 2014, DOE presented the methodologies and 

results of the analyses set for in the NOPR TSD. Interested parties provided comments. Key 

issues raised by stakeholders included: (1) whether the energy model accurately predicts 

efficiency improvements; (2) the size restrictions and applications of 22-inch wide ice makers; 

(3) the efficiency distributions assumed for shipments of icemakers; and (4) the impact on 

manufacturers relating to design of icemaker models, in light of the proposed compliance date of 

3 years after publication of the final rule. 

In response to comments regarding the energy model used in the analysis, DOE held a 

public meeting on June 19, 2014 in order to facilitate an additional review of the energy model, 

gather additional feedback and data on the energy model, and to allow for a more thorough 

explanation of DOE’s use of the model in the engineering analysis. 79 FR 33877 (June 13, 

2014). At that meeting, DOE presented the energy model, demonstrated its operations, and 

described how it was used in the rulemaking’s engineering analysis. DOE indicated in this 

meeting that it was considering modifications to its NOPR analyses based on the NOPR 

comments and additional research and information gathering. 

On September 11, 2014, DOE published a notice of data availability (NODA) in the 

Federal Register (September 2014 NODA). 79 FR 54215. The purpose of the September 2014 

NODA was to notify industry, manufacturers, customer groups, efficiency advocates, 

government agencies, and other stakeholders of the publication of the updated rulemaking 

analysis for new and/or amended energy conservation standards for automatic commercial ice 

makers. Generally, the revisions to the NOPR analysis described in the NODA included 

modifications of inputs for its engineering, LCC, and NIA analyses; adjustments of its energy 

model calculations; and more thorough considerations of size-constrained ice maker 

applications. DOE encouraged stakeholders to provide comments and additional information in 

response to the September NODA publication, and opened a comment period which was open 

until October 14, 2014. 

DOE refined the NOPR and NODA analyses based on stakeholder comments for the final 

rule TSD.

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1.5 STRUCTURE OF THE DOCUMENT

Listed below are the 17 TSD chapters and related appendices that collectively form the 

TSD. 

Chapter 1 Introduction: provides an overview of the appliance standards program and how 

it applies to the ACIM rulemaking, provides a history of DOE’s actions to date, 

and outlines the structure of this document.

Chapter 2 Analytical Framework: describes the rulemaking process.

Chapter 3 Market and Technology Assessment: provides DOE’s definition of automatic 

commercial ice makers, discusses the proposed equipment classes, and names 

the major industry players. This chapter also provides an overview of ACIM

technology, including techniques employed to improve equipment efficiency.

Chapter 4 Screening Analysis: identifies all the design options that improve ACIM

efficiency, and determines which of these will be evaluated and which will be 

screened out.

Chapter 5 Engineering Analysis: presents detailed cost and efficiency information for the 

units of analysis. This chapter describes DOE’s approach for determining 

manufacturer costs, including the markups used for converting material costs to 

manufacturer sales prices.

Chapter 6 Markups for Equipment Price Determination: presents the methodology used to 

determine the distribution channel markups that are used to convert 

manufacturer selling prices into customer purchase price of the equipment.

Chapter 7 Energy and Water Use Analysis: DOE used the energy consumption model in 

the engineering analysis to estimate ACIM energy use. DOE did not conduct a 

separate energy use analysis for this rulemaking.

Chapter 8 Life-Cycle Cost and Payback Period Analysis: presents the methodology used 

to estimate the impact of potential new or amended standards on customers of 

automatic commercial ice makers by calculating LCC and PBP values at all 

higher efficiency levels.

Chapter 9 Shipments Analysis: presents the methodology used to estimate the historical

and future shipments of automatic commercial ice makers. The estimated 

shipments numbers are used as inputs to NIA and other downstream analyses.

Chapter 10 National Impact Analysis: presents the methodology used to estimate national 

impacts by calculating national energy savings and NPV at all higher efficiency 

levels.

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Chapter 11 Customer Subgroup Analysis: evaluates impacts on identifiable customer 

subgroups that may be disproportionately disadvantaged by the proposed new or 

amended standards.

Chapter 12 Manufacturer Impact Analysis: assesses the impacts on ACIM manufacturers of 

any new or amended standards. In addition to financial impacts, a wide range of 

quantitative and qualitative effects may occur following adoption of a standard 

that may require changes to the manufacturing practices for this equipment.

Chapter 13 Emissions Impact Analysis: presents the assessment of the impacts of proposed 

ACIM standard levels on emissions of certain pollutants.

Chapter 14 Monetization of Emission Reductions Benefits: presents the methodology to 

estimate monetary benefits likely to result from the reduced emissions of certain 

pollutants.

Chapter 15 Utility Impact Analysis: analyzes the effects of proposed new or amended 

ACIM standard levels on the electric utility industry.

Chapter 16 Employment Impact Analysis: estimates national job creation or elimination 

(indirect effects) resulting from possible amended standards due to reallocation 

of the associated commercial expenditures for purchasing and operating 

equipment.

Chapter 17 Regulatory Impact Analysis: evaluates potential major alternatives to standards 

to achieve customer product energy efficiency.

Appendix 6A Data for Equipment Price Markups: presents detailed data used for markups 

analysis.

Appendix 8A User Instructions for Life-Cycle Cost Spreadsheet: presents user instructions for 

the LCC spreadsheet.

Appendix 8B Detailed Life-Cycle Cost and Payback Period Analysis Results: presents 

detailed results from the LCC analysis.

Appendix 8C Uncertainty and Variability in Life-Cycle Cost Analysis: presents brief 

discussion on the uncertainty and variability analysis used in the LCC analysis.

Appendix 8D Estimation of Potential Equipment Price Trend for Automatic Commercial Ice 

Makers: presents experiential learning analysis in the LCC analysis.

Appendix 10A User Instructions for NIA Spreadsheet: presents user instruction for the NIA 

spreadsheet.

Appendix 10B National Net Present Value Using Alternative Sensitivity Analyses: presents 

experiential learning sensitivity analysis results, electric price sensitivity 

analysis results, and 9-year analysis results.

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Appendix 10C Trial Standard Levels and Standards Equations: presents the criteria for trial 

standard levels (TSLs) selection and the proposed standards equation at each 

TSL.

Appendix 10D Full-Fuel-Cycle Multipliers: presents the development of full-fuel-cycle 

coefficients.

Appendix 10E RISC & OIRA Consolidated Information System (ROCIS) Tables: presents the 

ROCISg tables.

Appendix 12A Government Regulatory Impact Model: presents an overview of the model used 

in the MIA.

Appendix 14A Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 

12866: presents the SCC analysis.

Appendix 14B Technical Update of the Social Cost of Carbon for Regulatory Impact Analysis -

Under Executive Order 12866: presents updated SCC analysis.

g Regulatory Information Service Center (RISC) and Office of Information and Regulatory Affairs (OIRA) 

Combined Information System.

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REFERENCES

1. U.S. Department of Energy–Energy Information Administration. Annual Energy Outlook 

2014. 2014. Washington D.C. DOE/EIA-0383(2014).

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CHAPTER 2. ANALYTICAL FRAMEWORK

TABLE OF CONTENTS

2.1 INTRODUCTION ........................................................................................................... 2-1

2.2 BACKGROUND ............................................................................................................. 2-4

2.3 MARKET AND TECHNOLOGY ASSESSMENT........................................................ 2-5

2.3.1 Market Assessment .............................................................................................. 2-5

2.3.2 Technology Assessment....................................................................................... 2-5

2.4 SCREENING ANALYSIS .............................................................................................. 2-5

2.5 ENGINEERING ANALYSIS.......................................................................................... 2-6

2.5.1 Analysis Approach............................................................................................... 2-6

2.5.2 Baseline Models................................................................................................... 2-7

2.6 MARKUPS TO DETERMINE EQUIPMENT PRICE ................................................... 2-7

2.7 ENERGY AND WATER USE ANALYSIS ................................................................... 2-8

2.8 LIFE-CYCLE COST AND PAYBACK PERIOD ANALYSIS ..................................... 2-8

2.9 SHIPMENTS ANALYSIS ............................................................................................ 2-10

2.10 NATIONAL IMPACT ANALYSIS.............................................................................. 2-10

2.11 CUSTOMER SUBGROUP ANALYSIS....................................................................... 2-12

2.12 MANUFACTURER IMPACT ANALYSIS.................................................................. 2-12

2.13 EMISSIONS ANALYSIS.............................................................................................. 2-13

2.14 MONETIZING CARBON AND OTHER EMISSIONS REDUCTIONS BENEFITS. 2-14

2.15 UTILITY IMPACT ANALYSIS................................................................................... 2-15

2.16 EMPLOYMENT IMPACT ANALYSIS....................................................................... 2-16

2.17 REGULATORY IMPACT ANALYSIS ....................................................................... 2-16

2.18 DEPARTMENT OF JUSTICE REVIEW ..................................................................... 2-16

REFERENCES .......................................................................................................................... 2-17

LIST OF FIGURES

Figure 2.1.1 Analyses for Automatic Commercial Ice Maker Energy Conservation Standards. 2-3

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CHAPTER 2. ANALYTICAL FRAMEWORK

2.1 INTRODUCTION

This chapter describes the analytical framework that the U.S. Department of Energy 

(DOE) used in developing standards and assessing the impact for automatic commercial ice 

makers. The chapter describes the methodology, analytical tools, and relationships among the 

various analyses that are part of this rulemaking. The objective of the rulemaking process is to 

determine minimum efficiency standards for automatic commercial ice maker (ACIM)

equipment that are technologically feasible and economically justified. In this context, economic 

justification includes consideration of the economic impact on customers and manufacturers, the 

national benefits, the impacts on utilities, and the impacts from any lessening of competition.

Figure 2.1.1 summarizes the analytical components of the standards-setting process. The 

focus of this figure is the third column, identified as “Analysis.” The columns labeled “Key 

Inputs” and “Key Outputs” indicate how the analyses fit into the rulemaking process, and how 

the analyses relate to each other. Key outputs are analytical results that feed directly into the 

standards-setting process. Dotted lines connecting analyses indicate types of information that 

feed from one analysis to another. Key inputs are the types of data and information that the 

analyses require. Some key inputs exist in public databases, and DOE will also collect inputs 

from stakeholders or others with special knowledge. Inputs developed by the project team for the 

standards-setting process are presented and open for stakeholder review.

The analyses that DOE performed for the final rule include

• a market and technology assessment to characterize the automatic commercial ice 

makers’ market and to review techniques and approaches used to produce moreefficient ACIM equipment;

• a screening analysis to identify design options that improve ACIM equipment 

efficiency and to determine which should be evaluated and which should be screened 

out;

• an engineering analysis to estimate the relationship between the manufacturing cost 

of an ACIM unit and its performance;

• a markup analysis to convert manufacturer selling prices (MSPs) to customer 

purchase prices; 

• an energy and water use analysis to estimate the energy and water consumption of the 

equipment;

• a life-cycle cost (LCC) and payback period (PBP) analysis to estimate the impact of 

potential new or amended standards on customers of ACIM equipment by calculating 

LCC and PBP values at all higher efficiency levels;

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• a shipments analysis to estimate shipments of ACIM equipment over the time period 

examined in the analysis; 

• a national impact analysis (NIA) to assess the aggregate impacts at the national level 

of net present value (NPV) of total customer savings and national energy savings 

(NES);

• a customer subgroup analysis to evaluate impacts on identifiable groups of customers 

who may be disproportionately affected by new or amended standards;

• a manufacturer impact analysis (MIA) to estimate the financial impact of potential 

amended standards on ACIM equipment manufacturers and to calculate impacts on 

competition, employment at the manufacturing plant, and manufacturing capacity;

• an emissions analysis to provide estimates of the effects of amended energy 

conservation standards on emissions of carbon dioxide (CO2), sulfur dioxide (SO2), 

nitrogen oxides (NOx), and mercury (Hg); 

• a monetization of reduction of emission benefits from proposed standards; 

• a utility impact analysis to estimate the effects of proposed standards on the installed 

capacity and generating base of electric utilities;

• an employment impact analysis to estimate the impacts of amended standards on net 

jobs eliminated or created in the general economy as a consequence of increased 

spending on the purchase price of ACIM equipment and reduced customer spending 

on energy; and

• a regulatory impact analysis (RIA) to explore major alternatives to proposed 

standards that could achieve comparable energy savings.

The analyses and results published by DOE in the final rule reflect comments and input 

received subsequent to the publication of a notice of proposed rulemaking (NOPR) in March 

2014 and a notice of data availability (NODA) in September 2014.

2-2
Figure 2.1.1 Analyses for Automatic Commercial Ice Maker Energy Conservation 

Standards

2-3
2.2 BACKGROUND

As described in chapter 1 of this technical support document (TSD), DOE announced a 

formal effort to consider further improvements to the process used to develop appliance 

efficiency standards. DOE called on energy efficiency groups, manufacturers, trade associations, 

state agencies utilities, and other interested parties to provide input to this effort. As a result of 

this combined effort, DOE published Procedures, Interpretations and Policies for Consideration 

of New or Revised Energy Conservation Standards for Consumer Products (the “Process Rule”), 

10 CFR part 430 subpart C, appendix A. The Process Rule outlined the procedural improvements 

identified by the interested parties and included a review of the (1) economic models; 

(2) analytical tools; (3) methodologies; (4) non-regulatory approaches; and (5) prioritization of 

future rules. The Process Rule recommended that DOE take into account uncertainty and 

variability by carrying out scenario or probability analysis. Consistent with the improved 

rulemaking framework of the Process Rule, the following sections provide a general description 

of the analytical components for this rulemaking.

DOE developed the analytical framework pertaining to automatic commercial ice makers 

in the Rulemaking Framework for Automatic Commercial Ice Making Equipment (Nov. 4, 2010). 

DOE announced the availability of the Framework document in a notice of public meeting and 

availability of a Framework document published in the Federal Register. 75 FR 70852 (Nov. 19, 

2010) A copy of the framework document is available at 

www1.eere.energy.gov/buildings/appliance_standards/rulemaking.aspx/ruleid/29

DOE presented the analytical approach to interested parties during a public meeting held 

on December 16, 2010.1 DOE used comments gathered during the Framework public meeting as 

well as additional information during the preliminary analysis stage, at the completion of which

DOE announced the notice of public meeting and the availability of the preliminary TSD.

2 77 FR 

3404 (Jan. 24, 2012). DOE subsequently held a public meeting on February 16, 2012 to present 

the analysis and to receive comments.

DOE published a NOPR on March 17, 2014 (79 FR 14846) and held a public meeting on 

April 14, 2014 to present the NOPR analyses and to seek public comment. The NOPR analysis 

and TSD are available at 

www1.eere.energy.gov/buildings/appliance_standards/rulemaking.aspx/ruleid/29

DOE also held a public meeting on June 19, 2014 to provide stakeholders more 

information regarding the energy modeling used as part of its engineering analysis—this meeting 

was announced in a notice of proposed meeting (NOPM), which was published June 13, 2014. 

79 FR 33877.

DOE published a notice of data availability (NODA) on September 11, 2014. 79 FR at 

54215. The NODA provided stakeholders with rulemaking analyses conducted after the NOPR 

for automatic ice makers.

This TSD contains details of the final rule analyses conducted for automatic commercial 

ice makers. The remainder of chapter 2 provides a brief overview of the analytical approaches

for the final rule phase analysis of this rulemaking. 

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2.3 MARKET AND TECHNOLOGY ASSESSMENT

The market and technology assessment characterizes the ACIM equipment market and 

existing technology options for making a unit of ACIM equipment more efficient. 

2.3.1 Market Assessment

DOE reviewed relevant literature and interviewed manufacturers to develop an overall 

picture of the ACIM industry in the United States. Industry publications and trade journals, 

government agencies, and trade organizations provided the bulk of the information, including 

(1) manufacturers and their market shares; (2) shipments by product type and capacity; 

(3) product information; and (4) industry trends. 

When evaluating and establishing energy conservation standards, DOE generally divides 

covered equipment into equipment classes by the type of energy used, capacity, or other 

performance-related features that affect efficiency. Different energy conservation standards may 

apply to different equipment classes. (42 U.S.C. 6295(q) and 6313(d)(4))

When initiating a standards rulemaking, DOE develops information on the present and 

past industry structure and market characteristics of the equipment concerned. This activity 

consists of both quantitative and qualitative efforts to assess the industry and equipment based on 

publicly available information. 

2.3.2 Technology Assessment

The function of the technology assessment is to develop a preliminary list of technologies 

that could potentially be used to improve the efficiency of ACIM equipment, as well as to 

highlight the developments within those technology categories and their applicability to these 

equipment classes. The result of technology assessment is a list of technology options to be 

analyzed in the screening analysis. Chapter 3 of the final rule TSD includes a detailed list of all 

technology options DOE identified for this rulemaking.

2.4 SCREENING ANALYSIS

The purpose of the screening analysis is to evaluate the technologies identified in the 

technology assessment to determine which options to consider further in the engineering analysis

and which options to screen out. As stated in section 2.3, DOE consulted with industry, technical 

experts, and other interested parties in developing a list of energy-saving technologies for the 

technology assessment. DOE then applied screening criteria in the screening analysis to 

determine which technologies were unsuitable for further consideration in this rulemaking. 

These screening criteria examine whether various technologies (1) are technologically 

feasible; (2) are practicable to manufacture, install, and service; (3) have an adverse impact on 

product utility or availability; and (4) have adverse impacts on health and safety. Chapter 4 of the

final rule TSD, the screening analysis, contains details about DOE’s screening criteria.

For the efficiency-enhancement technologies that are not eliminated in the screening 

analysis, DOE conducts further analysis on these technologies in the engineering analysis. 

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2.5 ENGINEERING ANALYSIS

The purpose of the engineering analysis (chapter 5 of the final rule TSD) is to determine 

the relationship between manufacturer production cost (MPC) and energy and/or water 

consumption for automatic commercial ice makers. In determining this relationship, DOE 

estimates the increase in MPC associated with design changes that decrease energy and/or water

consumption as compared with baseline-efficiency models. In addition, DOE also identifies the 

model with the lowest energy consumption that is technologically feasible within each 

equipment class (i.e., the “max-tech” model) in the engineering analysis. While this rulemaking 

also addresses condenser water consumption, the regulation of condenser water use for ice 

makers is intended primarily to prevent excess water waste associated with attempts to reduce 

energy use by increasing condenser water use. Hence, the engineering analysis does not identify 

maximum technology levels for condenser water consumption.

In general, the engineering analysis estimates the efficiency improvement potential of the 

individual or combinations of design options that passed the four criteria in the screening 

analysis. DOE, in consultation with stakeholders, uses the most appropriate method to determine 

the manufacturing cost of these design options. DOE recognizes that some of the design options 

will reduce both energy and water consumption, but others may reduce one and increase the 

other. To address this issue, DOE developed an approach to consider both energy use and 

condenser water use in the engineering analysis in a way that maintains the rulemaking’s focus 

on energy use reduction while appropriately accounting for the cost implications of changing 

water use. This MPC-efficiency relationship developed in the engineering analysis is used in the 

LCC analysis.

DOE develops MPC estimates for the engineering analysis disaggregated into the cost 

contributions of materials (including purchased parts), labor, and overhead. MSP combines MPC 

and a manufacturer markup for overhead and profit. This information is used in DOE’s

downstream analyses, including the LCC, MIA, and NIA. DOE creates an industry-wide analysis 

based on a variety of data sources, including manufacturer-supplied data. 

2.5.1 Analysis Approach

DOE has developed three basic approaches to develop the relationship between cost and 

performance: design-option, efficiency-level, and reverse-engineering/cost-assessment.

A design-option approach uses individual design options, or combinations of design 

options, to identify increases in efficiency. Under this approach, cost estimates are based on 

manufacturer or component supplier data or engineering computer simulation models. Individual 

design options, or combinations of design options, are added to the baseline model in ascending 

order of cost-effectiveness. Also, the efficiency levels addressed in the analyses, including the 

downstream analyses, are based on groups of design options.

An efficiency-level approach establishes the relationship between manufacturer cost and 

increased efficiency at predetermined efficiency levels above the baseline. Under this approach, 

manufacturers typically provide manufacturer cost data for incremental increases in efficiency, 

without identifying the technology or design options they would use to achieve such increases.

2-6
A reverse-engineering or cost-assessment approach involves purchasing representative 

units of ACIM equipment, disassembling the units, and reverse engineering the manufacturing 

costs based on a “bottoms-up” manufacturing cost assessment. The efficiency levels addressed in 

the analyses for such an approach are based on the efficiency levels of the reverse-engineered 

products.

DOE used a combination of all three of these approaches to determine the cost-efficiency 

relationship for this rulemaking. DOE obtains information from a variety of sources, including 

consultation with outside experts, review of publicly available cost and performance information, 

modeling of equipment cost and energy consumption, interviews with manufacturers, product 

teardowns and reverse engineering, and data submissions provided by the industry. 

2.5.2 Baseline Models 

In order to analyze design options for energy efficiency improvements, DOE defines a 

baseline model unit for each equipment class. As DOE indicated in the Framework document,

the baseline efficiency levels for equipment currently covered by the DOE standards formed the 

baseline efficiency level for batch type equipment.a For the continuous type ice makers that are 

not currently covered by DOE’s standards, DOE analyzed available data on continuous type 

equipment, including newly available information on ice hardness data. Based on the available 

data, DOE identified the least efficient available for each continuous class. This formed the 

baseline energy usage for continuous type equipment classes and aided the determination of

harvest capacity break points delineating large and small equipment classes within each 

continuous equipment type.

2.6 MARKUPS TO DETERMINE EQUIPMENT PRICE

DOE uses the markup analysis to determine distribution channel markups that are used to 

convert the MSP of the equipment into customer purchase price. DOE uses customer purchase 

price to analyze the impact of potential energy conservation standard levels from the consumer 

perspective. To estimate the cost to customers buying ice makers, DOE adds markups to reflect 

the costs added by the wholesale, distribution, and contractor communities that distribute, sell, 

and install equipment.

In this rulemaking, DOE identified three different major channels through which the 

customers purchase automatic commercial ice makers. DOE then determined the market shares 

of each distribution channel and calculated the overall markup as a weighted average of the three 

channels. Sales tax is added to the markup associated with distribution channels. 

DOE calculated baseline markups that were applied to baseline MSPs and incremental 

markups that were applied to MSP increments at higher efficiency levels. See TSD chapter 6 for 

details on the markups analysis. 

a Batch type equipment includes cube-type, tube-type, and others that produce ice in alternative cycles of freezing 

and harvesting. Continuous type equipment, on the other hand, produces ice continuously. Continuous type ice 

makers include flake and nugget machines.

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2.7 ENERGY AND WATER USE ANALYSIS 

In the energy and water use analysis, DOE typically reviews the engineering results to 

assess their use in the downstream analyses and whether additional analyses are required. For 

automatic commercial ice makers, DOE concluded no additional analyses were necessary. Thus, 

the energy and water usage analysis was limited to converting the engineering estimates of 

kilowatt-hours per 100 lb ice and gallons per 100 lb ice into annual energy and water usage 

values.

TSD chapter 7 provides detailed information on the annual energy and water analysis for 

this rulemaking. 

2.8 LIFE-CYCLE COST AND PAYBACK PERIOD ANALYSIS

DOE carried out the LCC and PBP analysis to evaluate the economic impacts of possible 

amended energy conservation standards developed for automatic commercial ice makers on 

individual commercial customers. The effects of amended standards on individual customers 

usually include a reduction in operating cost and an increase in purchase cost, which together 

determine the economic impact of standards on individual customers. DOE analyzes two metrics 

to measure this impact:

• Life-cycle cost. LCC is the total customer cost of an appliance or piece of equipment, 

generally over the life of the appliance or equipment—the sum of installed cost and 

operating costs. Future operating costs are discounted to the time of the purchase and 

summed over the lifetime of the equipment.

• Payback period. PBP measures the amount of time it takes customers to recover the 

assumed higher purchase price of a more energy-efficient piece of equipment through 

lower operating costs.

As part of the engineering analysis (chapter 5 of the final rule TSD), DOE explored 

various efficiency levels based on increasing efficiency (decreased energy consumption) and, 

typically, increasing MSP values. For the LCC and PBP analysis, DOE treats each higher 

efficiency level as a potential new standard level. 

The first efficiency level (level 1) in each equipment class is the least efficient and the 

least expensive equipment in that equipment class. For the equipment classes that are currently 

covered by DOE’s standards, level 1 equipment is equipment that just meets the efficiency 

standards. For equipment not currently covered by standards, level 1 reflects the least efficient 

equipment currently available in the ACIM market. The higher efficiency levels (level 2 and up) 

have progressive increases in efficiency and equipment cost from level 1. The highest efficiency 

level in each equipment class corresponds to the maximum efficiency level obtainable with nonproprietary technology (see final rule TSD chapter 5 for details).

Inputs to the LCC calculation include the installed customer cost of the equipment 

(customer purchase price plus installation costs and sales tax), and operating expenses (repair, 

maintenance, energy, and water/wastewater costs), future price trends, equipment lifetimes, and 

discount rates.

2-8
The installed cost of equipment to a customer is the sum of the equipment purchase price 

and installation cost. The purchase price includes the MSP plus markups to the MSP to account 

for distribution costs and sales tax (chapter 6 of the final rule TSD). Installation costs vary by 

state depending on the prevailing labor rates. 

Operating costs for automatic commercial ice makers are the sum of maintenance costs, 

repair costs, energy costs, and water costs. Customers incur these costs over the life of the 

equipment. To facilitate cost comparisons, DOE discounted operating costs to the discount year 

(2018, the effective date of the standards established through this rulemaking). The sum of the 

installed cost and the operating costs, discounted to reflect the present value, is termed the lifecycle cost or LCC.

Generally, customers incur higher installed costs when they purchase higher efficiency 

equipment, and these cost increments will be partially or wholly offset by savings in the 

operating costs over the lifetime of the equipment. Usually, the savings in operating costs are due 

to savings in energy costs because higher efficiency equipment uses less energy over the lifetime 

of the equipment. DOE calculated the change in LCC for each efficiency level of each equipment 

class.

DOE obtained the PBP of higher efficiency equipment by dividing the increase in the 

installed cost by the decrease in annual operating cost. For this calculation, DOE used the sum of 

the first year operating cost changes as the estimate of the annual decrease in operating cost, 

noting that some of the repair and maintenance costs used herein are annualized estimates of 

costs. DOE calculated a PBP for each efficiency level of each equipment class.

The LCC and PBP model are developed using Microsoft Excel spreadsheets combined 

with Crystal Ball, a commercially available add-in for modeling uncertainty. DOE estimated 

many inputs for the LCC and PBP analysis from the best available data, and in some cases DOE 

used inputs that are generally accepted values within the automatic commercial ice maker 

industry. In general, there is uncertainty associated with many inputs as it is often difficult to 

obtain a single representative value for a given input. Therefore, DOE carried out the LCC and 

PBP analysis in the form of Monte Carlo simulations using ranges of values and probability 

distributions for certain inputs that account for the uncertainties. DOE presents the results of the 

LCC and PBP analysis in the form of mean and median LCC savings; percentages of customers 

experiencing net savings, net cost, and no impact in LCC; and median PBP. For each equipment 

class, DOE carried out 10,000 Monte Carlo simulations.

DOE calculated LCC savings and PBP by comparing the installed costs and LCC values 

of a given standards scenario against those of the base-case scenario. The base-case scenario is 

the scenario in which customers purchase equipment in the absence of the proposed energy 

conservation standard. A standards-case scenario is a scenario in which customers purchase 

equipment after the hypothetical energy conservation standard goes into effect. The number of 

standards scenarios for an equipment class is equal to one less than the total number of efficiency 

levels in that equipment class because each efficiency level above level 1 represents a potential 

new standard. Usually, the market will offer equipment at various efficiencies. Therefore, for 

both the base-case and the standards-case scenarios in the LCC and PBP analysis, DOE 

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calculated the market shares of the efficiency levels using a method described in final rule TSD 

chapter 10.

Different types of buildings and industries face different energy prices and apply different

discount rates to purchase decisions. DOE analyzed variability and uncertainty by performing the 

LCC and PBP calculations for seven types of buildings: health care, lodging, foodservice, retail,

education, food sales, and offices. 

The typical equipment lifetime is approximately 7 to 10 years with an average of 8.5 

years. There was no data to suggest that lifetimes are unique to each equipment class. Therefore, 

DOE assumed a distribution of equipment lifetimes that is defined by Weibull survival functions, 

with an average value of 8.5 years.

Another important factor influencing the LCC and PBP analysis is the location in which 

the automatic commercial ice maker is installed. Inputs that vary by location include installation 

costs, water and energy prices, distribution chain markups, and sales tax. At the national level, 

DOE explicitly modeled variability in water price, electricity price, and markups using 

probability distributions based on the relative populations in all states. 

Final rule TSD chapter 8 provides detailed information on DOE’s LCC and PBP analysis 

for this rulemaking. Results of the LCC and PBP analysis are presented at the end of final rule

TSD chapter 8 and in appendix 8B.

2.9 SHIPMENTS ANALYSIS

The shipments analysis is used to estimate future equipment shipments as well as the 

current stock of ACIM equipment. It provides crucial input for estimating the NES, NPV, and 

the MIA. Shipments analysis is used to estimate future ACIM equipment shipments over the 

national impact analysis period.

DOE calculated the historical shipments and stock of ACIM equipment for the year 2010

and earlier based on various shipments data sources. DOE then used the 2014 Annual Energy 

Outlook3 (AEO2014) forecasts of commercial floor space additions, by business type, to 

calculate the future shipments. 

TSD chapter 9 presents the mathematical formulation of the shipment analysis model and 

the methodology used to estimate historical and future shipments of ACIM equipment.

2.10 NATIONAL IMPACT ANALYSIS

DOE quantifies national impacts of energy conservation standards using two metrics: 

(1) NES and (2) NPV of future savings. NES and NPV impacts are the cumulative energy and 

economic effects of an amended ACIM equipment energy conservation standard (TSD 

chapter 10). The NES values, expressed in quads (quadrillion British thermal units), are the 

cumulative energy savings as a result of the new standards for equipment purchased over the 30-

year time frame of DOE’s analysis, 2018–2047. The NPV of future savings is also calculated for 

equipment purchased from 2018 through 2047. NPV is expressed in constant 2013$ discounted 

to the present year (2014).

2-10
DOE analyzed energy savings, energy and water cost savings, equipment costs, 

maintenance and repair costs, and NPV of savings (or costs) for efficiency level developed in the 

engineering analysis. The national energy and cost savings (or increases) that would result from 

amended energy conservation standards depend on the projected energy savings per unit and the 

anticipated amount of equipment sold. DOE created base-case shipment projections that include 

units at various efficiency levels. It based the projections on historical information plus forecasts 

of market influences, national economic growth, and electricity consumption. DOE then derived 

energy savings for various CSLs from the cost-efficiency schedules.

To make the analysis more accessible and transparent to all stakeholders, DOE used an 

Excel spreadsheet model to calculate the NES and the NPV. Users can change input quantities 

within the spreadsheet to test the impact of alternative input assumptions. Unlike the LCC 

analysis, the NES spreadsheet does not use distributions for inputs or outputs. Users can 

demonstrate sensitivities by running different scenarios using the spreadsheet. 

As discussed in TSD chapter 10, the national impact analysis assesses the NPV of total 

consumer LCC and NES. DOE conducted an assessment of the aggregate impacts at the national 

level for the final rule. Analyzing impacts of Federal energy conservation standards requires a 

comparison of projected U.S. energy consumption with and without amended standards. The 

base case, which is the projected U.S. energy consumption without standard, includes the mix of 

efficiencies being sold at the time the standard becomes effective.

DOE estimated national energy consumption for each year beginning with the expected 

effective date of the standard. DOE calculated national annual energy savings as the difference 

between two projections: a base case and a standards case. 

DOE has historically presented NES in terms of primary energy savings. DOE has begun 

to also estimate full-fuel-cycle (FFC) energy savings. 76 FR 51282 (Aug. 18, 2011), as amended 

at 77 FR 49701 (Aug. 17, 2012). The FFC metric includes the energy consumed in extracting, 

processing, and transporting primary fuels, and thus presents a more complete picture of the 

impacts of energy efficiency standards. DOE’s approach is based on the calculation of an FFC 

multiplier for each of the energy types used by covered equipment. 

While DOE stated in that notice that it intended to use the Greenhouse Gases, Regulated 

Emissions, and Energy Use in Transportation (GREET) model to conduct the analysis, it also 

said it would review alternative methods, including the use of the DOE/Energy Information 

Administration’s (EIA’s) National Energy Modeling System (NEMS).b NEMS is a public 

domain, multi-sectored, partial equilibrium model of the U.S. energy sector. Each year, 

DOE/EIA uses NEMS to produce the Annual Energy Outlook. After evaluating both models and 

the approaches discussed in the August 18, 2011 notice, DOE has determined NEMS is a more 

appropriate tool for this application. 77 FR 49701 (Aug. 17, 2012). Therefore, DOE is using the 

NEMS model to conduct FFC analyses. For the final rule analysis, DOE calculated FFC energy 

b For more information on NEMS, refer to DOE EIA documentation. A useful summary is National Energy 

Modeling System: An Overview 2003, DOE/EIA-0581(2003), March 2003.

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savings using a methodology described in appendix 10B. Chapter 10 of this TSD presents both 

the primary NES and the FFC energy savings for the considered trial standard levels (TSLs).

2.11 CUSTOMER SUBGROUP ANALYSIS

The customer subgroup analysis in final rule TSD chapter 11 evaluates impacts on 

identifiable groups of customers of ACIM equipment who may be disproportionately 

disadvantaged by amended energy conservation standards. The LCC and PBP analysis described 

in chapter 8 of the final rule TSD is applied to seven major types of businesses, including health 

care, lodging, foodservice, retail, education, food sales, and offices, that use a majority of the 

ACIM equipment. Although the inputs for different types of businesses are different in the LCC 

and PBP analysis, the final results may not reflect the results experienced by certain customer 

subgroups. In other words, some of the adverse impacts on businesses that are disproportionately 

disadvantaged may be masked by the averaging effect of the LCC and PBP analysis. Therefore, 

DOE carried out the customer subgroup analysis by using the LCC and PBP analysis 

spreadsheet, but applying the inputs that are applicable only to the identified subgroups. 

In general, the subgroups that face higher cost of capital and lower electricity price rates 

are more disadvantaged than others. Higher cost of capital imposes burden on the businesses 

because they have to borrow additional capital to purchase equipment that meets new or 

amended standards, compared to the case of where there are no new or amended standards. 

Lower electricity price rates result in lower savings in energy costs and, consequently, lower 

LCC savings and higher PBPs. 

DOE carried out two LCC subgroup analyses, one each for foodservice and lodging, by 

using the LCC spreadsheet described in final rule TSD chapter 8 with certain modifications. The 

input for business type was fixed to the identified subgroup, which ensured that the discount 

rates and electricity rates associated with only that subgroup were selected in the Monte Carlo 

simulations (see TSD chapter 8). Additionally, a small business premium was added to the 

discount rate to reflect the higher discount rates faced by small businesses. Apart from these 

changes, all other inputs for LCC subgroup analysis were the same as those in the LCC analysis 

described in TSD chapter 8.

2.12 MANUFACTURER IMPACT ANALYSIS 

The MIA assesses the impacts of new energy conservation standards on manufacturers of 

the considered equipment. Potential impacts include financial effects, both quantitative and 

qualitative, that might lead to changes in the manufacturing practices for this equipment. DOE 

identified these potential impacts through interviews with manufacturers and other interested 

parties. 

DOE conducted the MIA in three phases and further tailored the analytical framework 

based on interested parties’ comments. In Phase I, an industry profile was created to characterize 

the industry, and a preliminary MIA was conducted to identify important issues that required 

consideration. In Phase II, an industry cash flow model and an interview questionnaire were 

prepared to guide subsequent discussions. In Phase III, manufacturers were interviewed, and the 

impacts of standards were assessed both quantitatively and qualitatively. Industry and subgroup 

2-12
cash flow and NPV were assessed through use of the Government Regulatory Impact Model 

(GRIM). Then impacts on competition, manufacturing capacity, employment, and cumulative 

regulatory burden were assessed based on manufacturer interview feedback and discussions. 

DOE discusses its findings from the MIA in chapter 12 of the TSD. 

2.13 EMISSIONS ANALYSIS 

In the emissions analysis, DOE estimated the reduction in power sector emissions of 

CO2, NOx, SO2, and Hg from potential energy conservation standards for ACIM equipment. In 

addition, DOE estimated emissions impacts in production activities (extracting, processing, and 

transporting fuels) that provide the energy inputs to power plants. These are referred to as 

“upstream” emissions. Together, these emissions account for the FFC. In accordance with 

DOE’s FFC Statement of Policy (76 FR 51282 (Aug. 18, 2011)), as amended at 77 FR 49701 

(Aug. 17, 2012), the FFC analysis includes impacts on emissions of methane (CH4) and nitrous 

oxide (N2O), both of which are recognized as greenhouse gases (GHGs).

DOE conducted the emissions analysis using emissions factors derived from data in 

EIA’s AEO2014, supplemented by data from other sources. DOE developed separate emissions 

factors for power sector emissions and upstream emissions. The method that DOE used to derive 

emissions factors is described in chapter 13 of the final rule TSD.

EIA prepares the AEO using the NEMS. Each annual version of NEMS incorporates the 

projected impacts of existing air quality regulations on emissions. AEO2014 generally represents 

current legislation and environmental regulations, including recent government actions, for 

which implementing regulations were available as of October 31, 2013.

SO2 emissions from affected electric generating units (EGUs) are subject to nationwide 

and regional emissions cap and trading programs. Title IV of the Clean Air Act sets an annual 

emissions cap on SO2 for affected EGUs in the 48 contiguous States and the District of Columbia 

(D.C.). SO2 emissions from 28 eastern states and D.C. were also limited under the Clean Air 

Interstate Rule (CAIR), which created an allowance-based trading program that operates along 

with the Title IV program in those states and D.C. 70 FR 25162 (May 12, 2005). CAIR was 

remanded to the U.S. Environmental Protection Agency (EPA) by the U.S. Court of Appeals for 

the District of Columbia Circuit (D.C. Circuit), but it remained in effect. See North Carolina v. 

EPA, 550 F.3d 1176 (D.C. Cir. 2008); North Carolina v. EPA, 531 F.3d 896 (D.C. Cir. 2008). In

2011 EPA issued a replacement for CAIR, the Cross-State Air Pollution Rule (CSAPR). 76 FR 

48208 (Aug. 8, 2011). On August 21, 2012, the D.C. Circuit issued a decision to vacate CSAPR. 

See EME Homer City Generation, LP v. EPA, 696 F.3d 7, 38 (D.C. Cir. 2012). The court ordered 

EPA to continue administering CAIR. The emissions factors used for today’s final rule, which 

are based on AEO2014, assume that CAIR remains a binding regulation through 2040.

The attainment of emissions caps is typically flexible among EGUs and is enforced 

through the use of emissions allowances and tradable permits. Under existing EPA regulations, 

any excess SO2 emissions allowances resulting from the lower electricity demand caused by the 

adoption of an efficiency standard could be used to permit offsetting increases in SO2 emissions 

by any regulated EGU. In past rulemakings, DOE recognized that there was uncertainty about 

the effects of efficiency standards on SO2 emissions covered by the existing cap-and-trade 

2-13
system, but it concluded that no reductions in power sector emissions would occur for SO2 as a 

result of standards.

Beginning in 2016, however, SO2 emissions will fall as a result of the Mercury and Air 

Toxics Standards (MATS) for power plants, which were announced by EPA on December 21, 

2011. 77 FR 9304 (Feb. 16, 2012). In the final MATS rule, EPA established a standard for HCl 

as a surrogate for acid gas hazardous air pollutants (HAP), and also established a standard for 

SO2 (a non-HAP acid gas) as an alternative equivalent surrogate standard for acid gas HAP. The 

same controls are used to reduce HAP and non-HAP acid gas; thus, SO2 emissions will be 

reduced as a result of the control technologies installed on coal-fired power plants to comply 

with the MATS requirements for acid gas. AEO2014 assumes that, in order to continue 

operating, coal plants must have either flue gas desulfurization or dry sorbent injection systems 

installed by 2015. Both technologies, which are used to reduce acid gas emissions, also reduce 

SO2 emissions. Under the MATS, NEMS shows a reduction in SO2 emissions when electricity 

demand decreases (e.g., as a result of energy efficiency standards). Emissions will be far below 

the cap established by CAIR, so it is unlikely that excess SO2 emissions allowances resulting 

from the lower electricity demand would be needed or used to permit offsetting increases in SO2

emissions by any regulated EGU. Therefore, DOE believes that efficiency standards will reduce 

SO2 emissions in 2015 and beyond.

CAIR established a cap on NOx emissions in eastern States and the District of Columbia. 

Energy conservation standards are expected to have little or no physical effect on these emissions 

in those States covered by CAIR because excess NOx emissions allowances resulting from the 

lower electricity demand could be used to permit offsetting increases in NOx emissions. 

However, standards would be expected to reduce NOx emissions in the states not affected by 

caps, so DOE estimated NOx emissions reductions from the standards considered in the final rule

for these states.

The MATS limit mercury emissions from power plants, but they do not include emissions 

caps and, as such, DOE’s energy conservation standards would likely reduce Hg emissions. DOE 

will estimate mercury emissions reduction using emissions factors based on AEO2014, which 

incorporates the MATS.

2.14 MONETIZING CARBON AND OTHER EMISSIONS REDUCTIONS BENEFITS

DOE considered the estimated monetary benefits from the reduced emissions of CO2 and 

NOx that are expected to result from each of the standard levels considered.

To estimate the monetary value of benefits resulting from reduced emissions of CO2, 

DOE used the most current Social Cost of Carbon (SCC) values developed and/or agreed to by 

an interagency process. The SCC is intended to be a monetary measure of the incremental 

damage resulting from GHG emissions, including, but not limited to, net agricultural 

productivity loss, human health effects, property damage from sea level rise, and changes in 

ecosystem services. Any effort to quantify and to monetize the harms associated with climate 

change will raise serious questions of science, economics, and ethics. But with full regard for the 

limits of both quantification and monetization, the SCC can be used to provide estimates of the 

social benefits of reductions in GHG emissions. 

2-14
The Interagency Working Group on SCC released an update of its previous report in 

2013.c The most recent estimates of the SCC in 2015, expressed in 2013$, are $12, $40.5, $62.4, 

and $119 per metric ton of CO2 avoided. For emissions reductions that occur in later years, these 

values grow in real terms over time. Additionally, the interagency group determined that a range 

of values from 7 percent to 23 percent should be used to adjust the global SCC to calculate 

domestic effects, although DOE gives preference to consideration of the global benefits of 

reducing CO2 emissions. 

DOE multiplies the CO2 emissions reduction estimated for each year by the SCC value 

for that year in each of the four cases. To calculate a present value of the stream of monetary 

values, DOE discounts the values in each of the four cases, using the discount rates that had been 

used to obtain the SCC values in each case.

DOE recognizes that scientific and economic knowledge continues to evolve rapidly as to 

the contribution of CO2 and other GHGs to changes in the future global climate and the potential 

resulting damages to the world economy. Thus, these values are subject to change. 

DOE also estimates the potential monetary benefit of reduced NOx emissions resulting 

from the standard levels it considers. For NOx emissions, estimates suggest a very wide range of

monetary values, ranging from $476 to $4,893 per ton in 2013$.

d In accordance with Office of 

Management and Budget (OMB) guidance,e DOE calculates a range of monetary benefits using 

each of the economic values for NOx and real discount rates of 3 percent and 7 percent.

2.15 UTILITY IMPACT ANALYSIS

DOE analyzed specific effects of its proposed standard levels on the electric utility 

industry as part of the final rule analyses, using a variant of the DOE EIA’s NEMS. The version 

of NEMS used for appliance standards analysis is called NEMS-Building Technologies (NEMSBT)f and is based on the AEO2014 Reference Case with minor modifications.

The utility impact analysis reports the changes in installed capacity and generation, by 

fuel type, that result from the adoption of new efficiency standards at each TSL, as well as 

changes in electricity consumption. See final rule TSD chapter 15 for more information on 

DOE’s utility impact analysis for this rulemaking. 

c Technical Update of the Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866. 

Interagency Working Group on Social Cost of Carbon, United States Government, May 2013. 

<www.whitehouse.gov/sites/default/files/omb/inforeg/social_cost_of_carbon_for_ria_2013_update.pdf>

d For additional information, refer to U.S. Office of Management and Budget, Office of Information and Regulatory 

Affairs, 2006 Report to Congress on the Costs and Benefits of Federal Regulations and Unfunded Mandates on 

State, Local, and Tribal Entities, Washington, D.C. e OMB, Circular A-4: Regulatory Analysis (Sept. 17, 2003).

f EIA approves use of the name NEMS to describe only an official version of the model without any modification to 

code or data. Because this analysis entails some minor code modifications and the model is run under various policy 

scenarios that are variations on EIA assumptions, DOE refers to it by the name NEMS-BT (BT is DOE’s Building 

Technologies Program, under whose aegis this work has been performed). NEMS-BT was previously called NEMSBRS.

2-15

 
2.16 EMPLOYMENT IMPACT ANALYSIS

The imposition of standards can affect employment both directly and indirectly. Direct 

employment impacts are changes in the number of employees at plants that produce covered 

equipment and at the affiliated distribution and service companies. DOE evaluates direct 

employment impacts in the MIA. Indirect employment impacts may result from customers 

shifting expenditures between goods (the substitution effect) and from changes in income and 

overall expenditure levels (the income effect). DOE used Pacific Northwest National 

Laboratory’s (PNNL’s) Impact of Sector Energy Technologies (ImSET) model to investigate the 

combined direct and indirect employment impacts. The ImSET model, which was developed for 

DOE’s Office of Planning, Budget, and Analysis, estimates the employment and income effects

that energy-saving technologies produce in buildings, industry, and transportation. Compared 

with simple economic multiplier approaches, ImSET allows for more complete analysis of the 

economic impacts of energy conservation investments.

2.17 REGULATORY IMPACT ANALYSIS

In the final rule stage, DOE prepared an RIA pursuant to Executive Order 12866, 

Regulatory Planning and Review, 58 FR 51735 (Oct. 4, 1993), which is subject to review under 

the Executive Order by the Office of Information and Regulatory Affairs at the OMB. The RIA 

addressed the potential for non-regulatory approaches to supplant or augment energy 

conservation standards to improve the energy efficiency or reduce the energy consumption of the 

ACIM equipment covered under this rulemaking.

2.18 DEPARTMENT OF JUSTICE REVIEW

Section 325(o)(2)(B)(i)(V) of the Energy Policy and Conservation Act states that before 

the Secretary of Energy may prescribe a new or amended energy conservation standard, the 

Secretary shall ask the Attorney General to make a determination of “the impact of any lessening 

of competition...that is likely to result from the imposition of the standard.” (42 U.S.C. 

6295(o)(2)(B)(i)(V); 6314(d)(4)) Pursuant to this requirement, DOE solicited the views of the 

Department of Justice (DOJ) on any lessening of competition that is likely to result from the 

imposition of a proposed standard. The DOJ reviewed the new and amended standards and 

determined that they are unlikely to have a significant impact on competition.

2-16
REFERENCES

1. U.S. Department of Energy—Office of Energy Efficiency and Renewable Energy Building 

Technologies Program. Rulemaking Framework for Automatic Commercial Ice-Makers. 

2010. Washington, D.C. 

<www1.eere.energy.gov/buildings/appliance_standards/rulemaking.aspx/ruleid/29 >

2. U.S. Department of Energy—Office of Energy Efficiency and Renewable Energy. 

Preliminary Technical Support Document: Energy Conservation Program for Certain 

Commercial and Industrial Equipment: Automatic Commercial Ice Makers. January 20, 

2012. Washington, D.C. 

<www1.eere.energy.gov/buildings/appliance_standards/rulemaking.aspx/ruleid/29 >

3. U.S. Department of Energy—Energy Information Administration. Annual Energy Outlook 

2014. 2014. Washington, D.C. DOE/EIA-0383(2014). 

2-17
CHAPTER 3. MARKET AND TECHNOLOGY ASSESSMENT

TABLE OF CONTENTS

3.1 INTRODUCTION .............................................................................................................. 3-1

3.2 MARKET ASSESSMENT................................................................................................. 3-3

3.2.1 Trade Association ................................................................................................... 3-3

3.2.2 Manufacturers and Market Share............................................................................ 3-4

3.2.2.1 Small Businesses...................................................................................... 3-6

3.2.2.2 Regulatory Programs ............................................................................... 3-6

3.2.2.3 Natural Resources Canada ....................................................................... 3-6

3.2.2.4 Australia/New Zealand ............................................................................ 3-7

3.2.3 Non-Regulatory Initiatives...................................................................................... 3-8

3.2.3.1 ENERGY STAR...................................................................................... 3-8

3.2.3.1 Federal Energy Management Program .................................................... 3-9

3.2.3.2 Consortium for Energy Efficiency........................................................... 3-9

3.2.3.3 Rebate Programs.................................................................................... 3-11

3.2.4 Equipment Classes................................................................................................ 3-12

3.2.5 Shipments and Available Equipment.................................................................... 3-13

3.2.5.1 Air-Conditioning, Heating, and Refrigeration Institute Data ................ 3-14

3.2.5.2 North American Association of Food Equipment Manufacturers Data. 3-15

3.2.5.3 Census Bureau Data............................................................................... 3-16

3.2.5.4 ENERGY STAR Data............................................................................ 3-16

3.2.6 Equipment Lifetimes............................................................................................. 3-17

3.3 TECHNOLOGY ASSESSMENT..................................................................................... 3-17

3.3.1 Baseline Equipment Components and Operation ................................................. 3-17

3.3.1.1 Basic Equipment Description and Components .................................... 3-18

3.3.1.2 Batch Process......................................................................................... 3-19

3.3.1.3 Continuous Process................................................................................ 3-24

3.3.2 Technology Options.............................................................................................. 3-26

3.3.2.1 Compressor ............................................................................................ 3-27

3.3.2.2 Condenser .............................................................................................. 3-29

3.3.2.3 Higher Efficiency Condenser Fans and Fan Motors.............................. 3-31

3.3.2.4 Improved Auger Motor Efficiency ........................................................ 3-31

3.3.2.5 Improved Pump Motor Efficiency......................................................... 3-31

3.3.2.6 Demand Response Controls................................................................... 3-31

3.3.2.7 Evaporator.............................................................................................. 3-32

3.3.2.8 Improved or Thicker Insulation ............................................................. 3-33

3.3.2.9 Larger Diameter Suction Line (Remote Compressor Models).............. 3-33

3.3.2.10 Reduced Potable Water Flow................................................................. 3-33

3.3.2.11 Drain Water Thermal Exchange ............................................................ 3-34

3.3.3 Energy Use Data ................................................................................................... 3-34

3.3.4 Condenser Water Consumption Data.................................................................... 3-46

REFERENCES .......................................................................................................................... 3-51

3-i
LIST OF TABLES

Table 3.1.1: Current Equipment Classes for Automatic Commercial Ice Makers ...................... 3-3

Table 3.2.1 NRCan Efficiency Standard for Continuous Automatic Commercial Ice Makers... 3-7

Table 3.2.2 ENERGY STAR Automatic Commercial Ice Maker Key Criteria for Air-Cooled 

Batch-Type Ice Makers........................................................................................ 3-8

Table 3.2.3 ENERGY STAR Automatic Commercial Ice Maker Key Criteria for Air-Cooled 

Continuous-Type Ice Makers............................................................................... 3-9

Table 3.2.4 Performance Requirement for Federal Purchases of Water-Cooled Ice Makers...... 3-9

Table 3.2.5 CEE Efficiency Requirements for Air-Cooled Ice Makers.................................... 3-11

Table 3.2.6 CEE Efficiency Requirements for Water-Cooled Ice Makers................................ 3-11

Table 3.2.7 Final Rule Automatic Commercial Ice-Making Equipment Classes...................... 3-13

Table 3.2.8 AHRI 2010 Shipments of Automatic Commercial Ice Makers, by Equipment 

Class................................................................................................................... 3-15

Table 3.2.9 Shipments of Ice Makers ........................................................................................ 3-16

Table 3.2.10 ENERGY STAR Ice Maker Shipments................................................................ 3-17

Table 3.2.11 Estimates for Automatic Commercial Ice Maker Lifetimes................................. 3-17

Table 3.3.1 Brief Description of Primary Refrigeration System Components.......................... 3-19

Table 3.3.2 Batch Evaporator Designs ...................................................................................... 3-21

Table 3.3.3 Technology Options for Automatic Commercial Ice Makers ................................ 3-27

Table 3.3.5 Compressor Rating Conditions (oF (oC))................................................................ 3-28

Table 3.3.6: Comparison of Current and Proposed Condenser Water Standards...................... 3-50

LIST OF FIGURES

Figure 3.2.1: Domestic Automatic Commercial Ice Maker Market Shares as of 1996............... 3-5

Figure 3.3.1 Typical Vapor Compression Refrigeration Cycle for Batch Type Ice Makers..... 3-20

Figure 3.3.2 Cracked Ice Forming on “Cracked Ice” Evaporators............................................ 3-22

Figure 3.3.3 Water System Diagram for Batch Type Ice Makers ............................................. 3-23

Figure 3.3.4 Typical Vapor Compression Refrigeration Cycle for Continuous Ice Makers..... 3-25

Figure 3.3.5 Cross-Section of Continuous Evaporator and Auger ............................................ 3-26

Figure 3.3.6 Energy Consumption vs. Harvest Rate (Air-Cooled Cube Ice-Making Heads).... 3-35

Figure 3.3.7 Energy Consumption vs. Harvest Rate (Water-Cooled Batch Ice-Making 

Heads) ................................................................................................................ 3-36

Figure 3.3.8 Energy Consumption vs. Harvest Rate (Air-Cooled Batch Remote 

Condensing Units [Non-Remote Compressor])................................................. 3-37

Figure 3.3.9 Energy Consumption vs. Harvest Rate (Air-Cooled Batch Remote 

Condensing Units [Remote Compressor])......................................................... 3-38

Figure 3.3.10 Energy Consumption vs. Harvest Rate (Air-Cooled Batch Self-Contained 

Units).................................................................................................................. 3-39

Figure 3.3.11 Energy Consumption vs. Harvest Rate (Water-Cooled Batch Self-Contained 

Units).................................................................................................................. 3-40

Figure 3.3.12 Energy Consumption vs. Harvest Rate (Air-Cooled Continuous Ice-Making 

Heads) ................................................................................................................ 3-41

Figure 3.3.13 Energy Consumption vs. Harvest Rate (Water-Cooled Continuous Ice-Making 

Heads) ................................................................................................................ 3-42

3-ii
Figure 3.3.14 Energy Consumption vs. Harvest Rate (Air-Cooled Continuous Remote 

Condensing Units with Non-Remote Compressors).......................................... 3-43

Figure 3.3.15: Energy Consumption vs. Harvest Rate (Air-Cooled Continuous Remote 

Condensing Units with Remote Compressors).................................................. 3-44

Figure 3.3.16 Energy Consumption vs. Harvest Rate (Air-Cooled Continuous 

Self-Contained Units) ........................................................................................ 3-45

Figure 3.3.17 Energy Consumption vs. Harvest Rate (Water-Cooled Continuous 

Self-Contained Units) ........................................................................................ 3-46

Figure 3.3.17: Condenser Water Consumption vs Harvest Rate (Water-Cooled 

Ice-Making Head Units) .................................................................................... 3-47

Figure 3.3.18: Condenser Water Consumption vs Harvest Rate (Water-Cooled Batch-Type 

Self-Contained Units) ........................................................................................ 3-48

Figure 3.3.19: Condenser Water Consumption vs Harvest Rate (Water-Cooled Continuous 

Ice-Making Head Units) .................................................................................... 3-49

Figure 3.3.20: Condenser Water Consumption vs Harvest Rate (Water-Cooled Continuous 

Self-Contained Units) ........................................................................................ 3-50

3-iii
CHAPTER 3. MARKET AND TECHNOLOGY ASSESSMENT

3.1 INTRODUCTION

This chapter details the market and technology assessment that the U.S. Department of 

Energy (DOE) has conducted in support of the energy conservation standards rulemaking for 

automatic commercial ice makers. Automatic commercial ice makers produce a range of ice 

types, including cube type ice, flake ice, nugget ice, tube type ice, and cracked or fragmented ice.

The Energy Policy and Conservation Act (42 U.S.C. 6311–6317; EPCA) prescribes energy 

conservation standards for automatic commercial ice makers that produce cube type ice with 

capacities between 50 and 2,500 pounds of ice per 24-hour period (lb/24 hours).

a (42 U.S.C. 

6313(d)(1)) EPCA required review of these standards prior to January 1, 2015. (42 U.S.C. 

6313(d)(3)) DOE is also establishing, under 42 U.S.C. 6313(d)(2), standards for other types of 

ice makers with capacities up to 4,000 lb/24 hours.

This chapter consists of a market and technology assessment. The purpose of the market 

assessment is to develop a qualitative and quantitative characterization of the automatic 

commercial ice maker industry and market structure based on publicly available information and 

information submitted by manufacturers and other stakeholders. Manufacturer characteristics and 

market shares, existing regulatory and non-regulatory efficiency improvement initiatives, 

equipment classes, and trends in markets and equipment characteristics are addressed. The 

purpose of the technology assessment is to develop a preliminary list of technologies that could 

improve the efficiency of automatic commercial ice makers.

Automatic commercial ice makers are used in several types of commercial sectors, 

including health care, lodging, foodservice, retail, education, food sales, and office buildings.

Definitions

EPCA defines “automatic commercial ice maker” as a factory-made assembly (not 

necessarily shipped in one package) that:

1. consists of a condensing unit and ice-making section operating as an integrated unit, 

with means for making and harvesting ice; and

2. may include means for storing ice, dispensing ice, or storing and dispensing ice. 

(42 U.S.C. 6311(19))

The Energy Policy Act of 2005 (EPACT 2005) set energy and condenser water usage 

standards for cube type icemakers capable of producing between 50 and 2,500 lb/24 hours, and 

added the definition of automatic commercial ice maker to EPCA. EPACT 2005 further 

established standards for automatic commercial ice makers that produce cube type ice based on:

a Pounds of ice per 24-hour period is abbreviated herein as lb/24 hours.

3-1

 
(1) the configuration of the ice-making and refrigeration systems; (2) the type of cooling media 

used; and (3) the capacity of the unit.

Accordingly, the main categories of equipment covered under this rulemaking are as 

follows.

1. Ice Making Head (IMH): An ice-making head is a category in which all of the icemaking machinery, including freezing, harvesting, and condensing components, are

integrated into one unit, called the ice making head. These units do not include a 

means for storing ice. Ice-making heads are generally mounted on top of storage bins

(which are sold separately).

2. Remote Condensing Ice Makers (RCU): For automatic commercial ice makers, two 

remote condensing configurations exist. In one configuration, the compressor is 

contained within the same cabinet as the ice freezing and harvesting equipment, while 

the condenser is located in a separate package. This configuration is referred to in this 

rulemaking as “remote condensing (but not remote compressor).” In the other 

configuration, the compressor is located remotely from the cabinet containing the ice 

freezing and harvesting equipment, typically with the condenser in a remote 

condensing unit. This configuration can also be designed for connection to an existing 

remote compressor rack and is referred to as “remote condensing and remote 

compressor” in this rulemaking. Ice makers equipped with remote condensing units 

are typically larger ice makers, with harvest capacities up to 4,000 lb/24 hours for 

commercial ice makers. For RCU units, the package containing the ice freezing and 

harvesting equipment may be called the ice making head portion of the unit.

3. Self-contained automatic commercial ice makers (SCU): A self-contained 

automatic commercial ice maker is a category in which the ice-making machinery, 

including freezing, harvesting, and condensing components, and storage 

compartment, are in an integral cabinet. Self-contained automatic commercial ice 

makers tend to have lower harvest capacities than other equipment types, are typically 

used in office buildings and retail applications, and are generally designed to fit under 

counters.

In addition to the foregoing categories of ice-making equipment, self-contained units and 

ice-making heads are also categorized by the means used for disposing of the heat extracted from 

water when making ice. 

1. Air Cooled: Ice makers are referred to as air cooled if the waste heat from the 

condenser is released directly into the air.

2. Water Cooled: Ice makers are referred to as water cooled if the heat removed from 

the condenser is released into a condenser water loop. Ice makers can be attached to 

closed cooling-water loops that recirculate and reuse the same cooling water; these 

installations require a cooling tower (or similar device) to cool the circulating water.

Conversely, ice makers can also be installed with open-loop (or single-pass) cooling 

systems that use water one time for cooling and then release it into the wastewater 

system. 

3-2
Most remote condensing ice makers feature air-cooled condensers, and the EPCA ice 

maker efficiency standards apply specifically to air-cooled equipment. DOE notes that at least 

one remote condensing ice maker features a water-cooled condenser.

The third differentiating factor within the equipment configuration and cooling type is the 

harvest capacity of a given ice maker. Harvest capacity is defined in terms of the amount of ice 

an ice maker can produce in 24 hours. 

DOE currently defines 13 different equipment classes for automatic commercial ice 

makers in 10 CFR 431 Subpart H (42 U.S.C. 6313(d)(1)). These are shown in Table 3.1.1

below.

Table 3.1.1: Current Equipment Classes for Automatic Commercial Ice Makers

Equipment Type Type of 

Cooling

Harvest Rate

lb ice/24 hours

Ice-Making Head

Water

<500

≥500 and <1,436

≥1,436

Air <450

≥450

Remote Condensing (but not remote 

compressor) Air <1,000

≥1,000

Remote Condensing and Remote 

Compressor Air <934

≥934

Self-Contained

Water <200

≥200

Air <175

≥175

3.2 MARKET ASSESSMENT

The following market assessment identifies the manufacturer trade association, domestic 

manufacturers of automatic commercial ice makers, manufacturer market share, regulatory 

programs, and non-regulatory initiatives; defines equipment classes; provides historical shipment 

data, shipment projections, and equipment lifetime estimates; and summarizes market 

performance data.

3.2.1 Trade Association

The Air-Conditioning, Heating, and Refrigeration Institute (AHRI)) is the U.S. trade 

association for automatic commercial ice maker manufacturers. A merger by the former AirConditioning and Refrigeration Institute, or ARI, and the former Gas Appliance Manufacturers 

Association, or GAMA resulted in the creation of AHRI. AHRI coordinates with member 

manufacturers to establish the Automatic Commercial Ice Makers and Ice Storage Bins division

3-3
within AHRI. This division serves to develop standards and implement a certification program 

for automatic commercial ice makers and storage bins.

Most manufacturers of automatic commercial ice makers are also members of the North 

American Association of Food Equipment Manufacturers (NAFEM). NAFEM is a trade 

association of foodservice equipment and supplies manufacturers providing products for food 

preparation, cooking, storage and table service. NAFEM represents the industry in Washington, 

D.C., supports programs and organizations that move the foodservice industry forward, and 

offers educational opportunities for foodservice professionals.

3.2.2 Manufacturers and Market Share

Current members of AHRI’s Automatic Commercial Ice Makers and Ice Storage Bins 

division are listed below; parent companies are shown in parentheses if applicable.

1

• Hoshizaki America, Inc.

• IMI Cornelius, Inc.

• ITV Ice Makers SA

• KD Industries, Inc./Kold Draft (Erie Management Group, LLC)

• Manitowoc Ice, Inc. (The Manitowoc Company, Inc.)

• Mile High Equipment LLC/Ice-O-Matic (Scotsman Industries)

• Scotsman Ice Systems (Scotsman Industries)

Other automatic commercial ice maker manufacturers are listed below; parent companies 

are shown in parentheses if applicable.

• A&V Refrigeration

• Arctic-Temp/Holiday Ice, Inc.

• Brema Ice Makers

• Follett Ice Makers (VGI Holdings Corp)

• Howe Corporation

• North Star Ice

• Orien U.S.A.

• Summit Appliance (Felix Storch, Inc.)

• Vogt Ice, LLC (Vogt Power International)

According to a recent study by Koeller & Company, the ice maker market is dominated 

by four manufacturers of automatic ice makers, who produce approximately 90 percent of the 

automatic commercial ice makers for sale in the United States.

2 The four major manufacturers

with the largest market share are Manitowoc (30 percent market share), Scotsman (25 percent), 

Hoshizaki (20 percent), and Mile High Equipment LLC/Ice-O-Matic (15 percent). See Figure 

3.2.1, where Koeller cited a 1996 report by Arthur D. Little, Inc.

3-4
Source: Koeller and Company, Potential Best Management Practices, June 2008.

Figure 3.2.1: Domestic Automatic Commercial Ice Maker Market Shares as of 1996

The landscape of the automatic commercial ice maker market has changed since this data 

was published in 1996. At about the same time that the data on Figure 3.2.1 was compiled, the 

parent company of Mile High Equipment LLC/Ice-O-Matic was acquired by Enodis, PLC. 

Enodis then acquired Scotsman industries in 1999.3 Thus, these two companies no longer existed 

as independent and separate entities. 

In 2008, Manitowoc agreed to purchase Enodis, PLC. At the time, Manitowoc was 

believed to control 40 percent of the market for cube-making machines, and Enodis controlled 

30 percent. The Manitowoc purchase of Enodis, PLC would have concentrated a vast majority of 

automatic commercial ice maker production in the hands of one company. In a settlement 

agreement with the U.S. Justice Department, Manitowoc agreed to divest the U.S. ice machine 

business lines previously held by Enodis, including Ice-O-Matic and Scotsman. Manitowoc sold

the ice machine business lines to Warburg Pincus Private Equity X, L.P., who announced they 

would use the Scotsman name.4,5 In November, 1992, Warburg Pincus announced they had 

agreed to sell Scotsman Industries to the ALI Group. Ice-O-Matic and Scotsman exist today as 

companies owned by the ALI Group.6

Manitowoc

30%

Scotsman

25%

Hoshizaki

20%

Mile High 

Equipment 

LLC/Ice-OMatic

15%

IMI Cornelius 

and Others

10%

3-5
3.2.2.1 Small Businesses

DOE has considered the possibility that energy conservation standards for automatic 

commercial ice makers would adversely affect small businesses. The Small Business 

Administration (SBA) defines small business manufacturing enterprises for the commercial 

refrigeration equipment, in general, as those having 750 employees or fewer.7 SBA lists small 

business size standards for industries as they are described in the North American Industry 

Classification System (NAICS). The size standard for an industry is the largest that a for-profit 

concern can be in that industry and still qualify as a small business for Federal Government 

programs. These size standards are generally expressed in terms of the average annual receipts or 

the average employment of a firm. For commercial refrigeration equipment,

b the size standard is 

matched to NAICS code 333415, Air-Conditioning and Warm Air Heating Equipment and 

Commercial and Industrial Refrigeration Equipment Manufacturing, and is 750 employees.

DOE has studied the potential impacts on these small businesses in detail during the 

manufacturer impact analysis, which were conducted as a part of the final rule analysis. In these 

analyses, DOE identified seven small business manufacturers of automatic commercial ice 

makers. More information on these analyses is available in Chapter 11 of the final rule TSD.

3.2.2.2 Regulatory Programs

Outside of the United States, Canada and Australia/New Zealand have efficiency 

standards for automatic commercial ice makers.

Within the United States, several states (Arizona, California, Oregon, and Washington)

have established efficiency regulations for automatic commercial ice makers. The state standards 

all regulate cube type ice makers only. California’s standards were adopted in 2004, and were

based on the Consortium for Energy Efficiency (CEE) Tier 1 and Federal guidelines for 

purchasing energy efficient equipment. The California standards also match those passed by the 

U.S. Congress as part of EPACT 2005. All other state standards were based on the 

California/EPACT 2005 standards, and became effective January 1, 2008. The state standards 

have since been pre-empted by the Federal standards set forth in EPACT 2005 and effective 

January 1, 2010. 

3.2.2.3 Natural Resources Canada

The Natural Resources Canada (NRCan) Office of Energy Efficiency established energy

efficiency standards for automatic commercial ice makers on December 31, 1998. The NRCan 

standard covers all equipment between 23 and 1,000 kilograms per day (between 50.6 and 2200 

b Automatic commercial ice makers is a small component of the equipment manufacturing category, NAICS code 

333415. This NAICS category covers a broad segment of heating, ventilation, and refrigeration equipment 

manufacturing including commercial refrigeration, commercial ice makers, heat pumps, air conditioners, warm air 

furnaces, air conditioners, and a number of other types of equipment and components.

3-6

 
lb per day)), and covers not just batch ice makers but also continuous ice (flake and nugget)

makers. NRCan’s standard had a compliance date of January 1, 2008. 

The NRCan standards are similar to the EPACT 2005 standards in that the covered batch 

equipment types are broken out into similar categories. However, NRCan standards cover 

additional equipment not covered by EPACT 2005 standards, including continuous type ice

makers and batch type ice makers that produce other than cube type ice. The NRCan efficiency 

standards also establish minimum storage effectiveness levels for ice storage bins on selfcontained ice makers. As stated in the final rule, DOE has not proposed standards for ice storage 

effectiveness levels.

For batch type ice makers, the NRCan standards are the same as those set by EPACT 

2005, but they are represented using harvest capacity expressed in terms of kilograms rather than 

pounds per 24 hours. Table 3.2.1 lists the NRCan equipment classes for continuous type

automatic commercial ice makers, with harvest capacity converted to lb/ 24 hours. 

NRCan does not require adjustments of continuous ice maker usage for ice hardness. Nor 

does the continuous ice maker efficiency standard differentiate by the type of equipment (i.e., 

ice-making head, self-contained, units with remote condensing). The NRCan website indicates 

the continuous process efficiency standard went into effect January 1, 2000.

Table 3.2.1 NRCan Efficiency Standard for Continuous Automatic Commercial Ice Makers

Equipment Type Type of 

Cooling

Ice-Making Capacity

kg/24 hours

(lb/24 hours*)

Maximum Energy

Use

kJ/kg

Maximum Energy

Use

kWh/100 lb**,***

All Continuous Machines Air < 300 (660)

≥ 300 (660)

875.2 – 1.122 x C

538.6

11.03 – 0.0064 x C

6.79

All Continuous Machines Water < 300 (660)

≥ 300 (660)

740.5– 0.8976 x C

471.2

9.33– 0.0051 x C

5.94

Source: NRCan website.

* The NRCan standard expressed capacity in terms of kilograms per 24 hours. DOE converted this to lb/24 hours 

for presentation in the table. 

** The NRCan standard expressed energy usage in terms of kJ/kg (kilojoule per kilogram). DOE converted this to 

kWh/100 lb for presentation in the table.

*** The NRCan standard expressed C as capacity (ice harvest rate) in kilograms per 24 hours. DOE converted 

this to kWh/100 lb for presentation in the table.

http://www.nrcan.gc.ca/energy/regulations-codes-standards/products/6893

3.2.2.4 Australia/New Zealand

Australia/New Zealand have also established required efficiency standards for automatic 

commercial ice makers and storage bins.

8 The minimum energy performance standards are 

established in Australian Standard AS/NZS 4865.3:2008 as maximum energy consumption per 

100 kg of ice. The Australian standards categorize equipment by the following criteria:

• modular type ice makers, air cooled and water cooled (two size categories)

• self-contained ice makers, air cooled and water cooled (two size categories)

• split system, air-cooled, remote condensing but not remote compressor

• split system, air-cooled, remote condensing and remote compressor

3-7
• storage systems

The standards apply to cube, flake, and nugget machines without distinguishing between 

the machines and, apparently, without adjusting continuous ice maker energy usage for ice 

hardness. The Australian standards also establish a high efficiency rating, above their minimum 

standard classes.

3.2.3 Non-Regulatory Initiatives

DOE reviewed several voluntary programs promoting energy efficient automatic

commercial ice makers in the United States, ENERGY STAR®, CEE, and the Federal Energy 

Management Program (FEMP) procurement program. DOE also reviewed various rebate 

programs offered by utilities.

3.2.3.1 ENERGY STAR

Since the publication of the preliminary analysis, the U.S. Environmental Protection 

Agency (EPA) has issued an updated set of ENERGY STAR standards. The ENERGY STAR

Version 2.0 standards issued by the EPA have been effective since February 1, 2013 and include

cube type and flake/nugget type air-cooled automatic ice machines. ENERGY STAR also sets a 

potable water use limit for all ice machines considered. These criteria are summarized in Table 

3.2.2 and Table 3.2.3.

EPA has implemented a single nonlinear formula showing the relationship of energy use 

with harvest capacity for each equipment type, instead of a piece-wise linear function with 

different formulas defined for different capacity ranges. By means of a more complicated 

formula, the ENERGY STAR specification creates a continuous curve while still respecting the 

asymptotic relationship between efficiency and harvest capacity.

Table 3.2.2 ENERGY STAR Automatic Commercial Ice Maker Key Criteria for AirCooled Batch-Type Ice Makers

Equipment Type Harvest Rate, H 

lb/24 hours

Energy Use Limit

kWh/100 lb ice

Potable Water Use Limit 

gal/100 lb ice

Air-Cooled 

IMH 200 ≤ H ≤ 1600 ≤ 37.72 * H -0.298 ≤20.0

RCU 400 ≤ H ≤ 1600 ≤ 22.95 * H -0.258 + 1.00 ≤20.0

1600 ≤ H ≤ 4000 ≤ -0.00011 * H + 4.60 ≤20.0

SCU 50 ≤ H ≤ 450 ≤ 48.66 * H -0.326 + 0.08 ≤25.0

kWh = kilowatt-hours, gal = gallons

Source: www.energystar.gov

3-8
Table 3.2.3 ENERGY STAR Automatic Commercial Ice Maker Key Criteria for AirCooled Continuous-Type Ice Makers

Equipment Type Energy Use Limit

kWh/100 lb ice

Potable Water Use Limit 

gal/100 lb ice

Air-Cooled

IMH ≤ 9.18 * H -0.057 ≤15.0

RCU ≤ 6.00 * H -0.162 + 3.50 ≤15.0

SCU ≤ 59.45 * H -0.349 + 0.08 ≤15.0

kWh = kilowatt-hours, gal = gallons

H = Harvest Rate

Source: www.energystar.gov

3.2.3.1 Federal Energy Management Program

Federal Energy Management Program (FEMP) is a program administered by DOE that 

oversees the Federal Government’s energy management and investment initiatives. FEMP has 

established purchasing specifications for energy efficient equipment, including cube type 

automatic commercial ice makers, which Federal agencies must follow when buying new 

equipment for their facilities.9 Federal purchasers are required by EPACT 2005 to purchase 

equipment that is either ENERGY STAR qualified or FEMP designated. The FEMP designated 

equipment consists of equipment that is in the upper 25 percent of energy efficiency in its class.

Table 3.2.4 provides the requirements for water-cooled ice makers.10 For air-cooled ice 

makers, FEMP refers users to the ENERGY STAR specifications (see Section 3.2.3.1).

Table 3.2.4 Performance Requirement for Federal Purchases of Water-Cooled Ice Makers

Type Ice Harvest Rate

lb/24 hours

Energy Use

kWh/100 lb*

Self-Contained Unit ≤100 8.1 or less

Self-Contained Unit 101 – 150 7.3 or less

Self-Contained Unit 151 - 200 6.6 or less

Self-Contained Unit ≥200 6.5 or less

Ice Making Head ≤300 5.3 or less

Ice Making Head 301 – 400 4.8 or less

Ice Making Head 401 – 500 4.3 or less

Ice Making Head 501 – 750 4.1 or less

Ice Making Head 751 – 1,435 3.5 or less

Ice Making Head ≥1,436 3.4 or less

Source: DOE Federal Energy Management Program Covered Products website, 

http://energy.gov/eere/femp/covered-product-category-water-cooled-icemachines. * Measured in accordance with ARI Standard 810-2007 with Addendum 1:

Performance Rating of Automatic Commercial/Ice Makers.

3.2.3.2 Consortium for Energy Efficiency

Between 2006 and July 2011, CEE used a three-tiered system to set efficiency 

requirements for cube type machines. CEE set Tier 1 to be equivalent to FEMP standards, most 

recently the FEMP standards effective as of January 1, 2010. CEE set Tier 2 to be 10 percent 

below Tier 1, and Tier 3 to be 15 percent below Tier 1. ENERGY STAR standards corresponded

3-9
approximately with Tier 2 (for air-cooled machines). CEE standards set limits on both energy 

and water use. The water use is separated into potable water use and cooling water use for watercooled machines.

Beginning on July 1, 2011, CEE revised their system, making the Tier 1 equal to the 

previous Tier 2 levels, and setting the new Tier 2 to 10 percent lower than Tier 1.11 The new 

CEE efficiency requirements for air-cooled ice makers are shown in Table 3.2.5, while the new 

requirements for water-cooled ice makers are shown in Table 3.2.6.

3-10
Table 3.2.5 CEE Efficiency Requirements for Air-Cooled Ice Makers

Level

Corresponding 

Base 

Specification

Equipment Type

Ice Harvest Rate 

(H) 

lb of ice/day

Energy Use 

Limit 

kWh/100 lb of 

ice

Potable 

Water Use 

Limit 

gal/100 lb of 

ice

Tier 1

ENERGY 

STAR / Former 

CEE Tier 2 

(pre-7/1/2011)

Cube Type Machines

Only: Ice-Making 

Head

<450 9.23 - 0.0077H ≤25

≥450 6.20 - 0.0010H ≤25

Cube Type Machines 

Only: RemoteCondensing without 

remote compressor

<1,000 8.05 - 0.0035H ≤25

≥,1000 4.64 ≤25

Cube Type Machines 

Only: RemoteCondensing with 

remote compressor

<934 8.05 - 0.0035H ≤25

≥934 4.82 ≤25

Cube Type Machines

Only: Self-Contained

<175 16.7 - 0.0436H ≤35

≥175 9.11 ≤35

Tier 2

Approximately

10% More 

Efficient than 

Average 

Performance

Cube and Nugget

Type Ice Machines

<175 14 – 0.0347H ≤30

≥175 and <450 9.6 – 0.0098H ≤20

≥450 and <1,000 5.9 – 0.0016H ≤20

≥1,000 4.5 – 0.0002H ≤20

Flake Type Ice

Machines

<1,000 6.5 – 0.0033H ≤20

≥1,000 3.2 ≤20

Source: http://library.cee1.org/content/cee-high-efficiency-specifications-commercial-ice-machines/

Table 3.2.6 CEE Efficiency Requirements for Water-Cooled Ice Makers

Level

Corresponding 

Base 

Specification

Equipment Type

Ice Harvest Rate 

(H) 

lb of ice/day

Energy Use 

Limit

kWh/100 lb ice

Potable 

Water Use 

Limit

gal/100 lb of 

ice

Tier 1

Former CEE 

Tier 2 (pre7/1/2011)

Cube Type Machines

Only: Ice-Making 

Head

<500 7.02 – 0.0049H ≤25

≥500 and <1436 5.13 – 0.0010H ≤25

≥1436 3.68 ≤25

Cube Type Machines

Only: Self-Contained

<200 10.6 – 0.0177H ≤35

≥200 7.07 ≤35

Tier 2

Approximately

10% More 

Efficient than 

Average 

Performance

Cube and Nugget

Type Ice Machines

<175 10.6 - 0.0241H ≤30

≥175 and <450 7.1 - 0.0062H ≤20

≥450 and <1000 4.7 - 0.0011H ≤20

≥1,000 3.7 - 0.0002H ≤20

Flake Type Ice

Machines

<1,000 4.8 - 0.0017H ≤20

≥1,000 3.1 ≤20

3.2.3.3 Rebate Programs

Numerous organizations and entities throughout the United States offer rebate programs 

for customers who purchase and install qualified automatic commercial ice makers. Twenty three

utilities, governmental agencies, and other entities operate rebate programs specifically to 

provide incentives to customers who purchase ENERGY STAR compliant ice makers. The 

rebate programs, which can be found on the ENERGY STAR website by using the “Commercial 

3-11
Food Service Equipment Incentive Finder,” offer rebates ranging from $50 to $600 for 

qualifying purchases. 

The EnergySmart Grocer Program is funded by California utility ratepayers under the 

auspices of the California Public Utilities Commission. Eligible participants include grocery and 

convenience stores, food processors, and refrigerated warehouses operating in the Pacific Gas 

and Electric Company (PG&E) electric service territory. The program offers rebates for use of 

specific technologies on automatic commercial ice makers that are equivalent to CEE Tier 3.

12

The New York State Energy Research and Development Authority (NYSERDA) offers 

rebates on pre-qualified commercial refrigeration equipment, including automatic commercial 

ice makers, ranging from $75 for smaller equipment meeting ENERGY STAR specifications to 

$175 for large equipment meeting ENERGY STAR specifications.13

3.2.4 Equipment Classes

Automatic commercial ice makers are divided into equipment classes categorized by 

physical characteristics that affect commercial application, equipment utility, and equipment 

efficiency: (1) the ice-making process; (2) the configuration of the ice-making and refrigeration 

systems; (3) the type of cooling media used; and (4) the capacity of the unit. The following list 

shows the key characteristics of automatic commercial ice makers that DOE uses to distinguish 

equipment classes:

1. ice-making process

• continuous

• batch

2. equipment configuration

• ice-making head

• remote condensing (but not remote compressor)

• remote condensing and remote compressor

• self-contained

3. condenser cooling media

• air-cooled

• water-cooled

4. capacity range

• various

Table 3.2.7 shows the automatic commercial ice-making equipment classes DOE is 

creating with this rulemaking. Note that for batch ice makers, the capacity ranges for these 

classes differ from those of the current ice maker classes as shown in Table 3.1.1. For the 

purposes of discussion of the different ice maker classes, the discussion in the TSD will refer to 

the current class distinctions for these batch ice maker classes unless otherwise indicated.

3-12
Table 3.2.7 Final Rule Automatic Commercial Ice-Making Equipment Classes

Type of Ice 

Maker Equipment Type

Type of 

Condenser 

Cooling

Harvest Capacity 

Rate

lb ice/24 hours

Batch

Ice-Making Head

Water

≥ 50 and < 300

≥ 300 and < 850

≥ 850 and < 1,500

≥ 1,500 and < 2,500

≥ 2,500 and < 4,000

Air

≥ 50 and < 300

≥ 300 and < 800

≥ 800 and < 1,500

≥ 1500 and < 4,000

Remote Condensing 

(but not remote 

compressor)

Air

≥ 50 and < 1,000

≥ 1,000 and < 4,000

Remote Condensing 

and Remote 

Compressor

Air

≥ 50 and < 942

≥ 942 and < 4,000

Self-Contained Unit

Water

≥ 50 and < 200

≥ 200 and < 2,500

≥ 2,500 and < 4,000

Air

≥ 50 and < 110

≥ 110 and < 200

≥ 200 and < 4,000

Continuous

Ice-Making Head

Water ≥ 50 and < 801

≥ 801 and < 4,000

Air

≥ 50 and < 310

≥ 310 and < 820

≥ 820 and < 4,000

Remote Condensing 

(but not remote 

compressor)

Air

≥ 50 and < 800

≥ 800 and < 4,000

Remote Condensing 

and Remote 

Compressor

Air

≥ 50 and < 800

≥ 800 and < 4,000

Self-Contained Unit

Water

≥ 50 and < 900

≥ 900 and < 2,500

≥ 2,500 and < 4,000

Air

≥ 50 and < 200

≥ 200 and < 700

≥ 700 and < 4,000

3.2.5 Shipments and Available Equipment

This section presents the shipments data obtained from AHRI, North American 

Association of Food Equipment Manufacturers (NAFEM), the U.S. Census Bureau, and 

ENERGY STAR.

3-13
3.2.5.1 Air-Conditioning, Heating, and Refrigeration Institute Data

As part of its comments on the Framework document, AHRI submitted 2010 shipment 

data by equipment class for its member companies, and historical information on total shipments. 

DOE used this shipments data as part of the basis for this analysis. DOE understands that this

data does not include the entire industry. As noted in section 3.2.2, AHRI membership includes 

some but not all manufacturers. However, AHRI’s members represent over 90 percent of the 

market, so the AHRI data covers the vast majority of the automatic commercial ice makers sold.

Table 3.2.8 shows 2010 annual shipments reported by AHRI for each category of 

automatic commercial ice maker by equipment class. Note that the AHRI data have been 

aggregated to match the primary equipment groupings, meaning that for batch and continuous ice 

maker types the remote condensing unit ice makers have been aggregated into one group, 

distinguished only by harvest rates. AHRI’s data include information to distinguish between 

remote condenser units with remote compressors and remote condenser units that do not have 

remote compressors.

3-14
Table 3.2.8 AHRI 2010 Shipments of Automatic Commercial Ice Makers, by Equipment

Class

Type of Ice Equipment 

Type

Type of 

Cooling

Harvest Capacity Rate

lb/24 hours 2010 Shipments

Batch IMH Water ≥50 and <500 7,364

Batch IMH Water ≥500 and <1,436 4,734

Batch IMH Water ≥1,436 and ≤4,000 748

Batch IMH Air ≥50 and <450 43,913

Batch IMH Air ≥450 and ≤4,000 26,173

Batch RCU Air ≥50 and <1,000 8,804

Batch RCU Air ≥1,000 and ≤4,000 9,856

Batch SCU Water ≥50 and <200 1,101

Batch SCU Water ≥200 and ≤4,000 351

Batch SCU Air ≥50 and <175 22,468

Batch SCU Air ≥175 and ≤4,000 10,640

Continuous IMH Water ≥50 and <1000 1,265

Continuous IMH Water ≥1,000 and <4,000 123

Continuous IMH Air ≥50 and <1000 7,213

Continuous IMH Air ≥1,000 and <4,000 260

Continuous RCU Air ≥50 and <1000 1,972

Continuous RCU Air ≥1,000 and <4,000 773

Continuous SCU Water ≥50 and <175 -

Continuous SCU Water ≥175 and <4,000 241

Continuous SCU Air ≥50 and <175 7,478

Continuous SCU Air ≥175 and <4,000 6,713

3.2.5.2 North American Association of Food Equipment Manufacturers Data

NAFEM prepares a biennial study of the foodservice equipment and supplies market. The 

latest available report is titled 2010 Size and Shape of the Industry Study.

14 It contains survey 

data from NAFEM members, including shipments numbers and the value of sales in dollars.

Based on the numbers reported by the respondents to the survey, NAFEM developed a total 

market estimate of shipments (units shipped and dollar sales) for the year 2009. NAFEM 

published similar study reports for shipments in 200815 and 2006.16

The “refrigeration and ice machine study” is part of the NAFEM reports and includes 

shipments data for cube, flake, and nugget ice makers as well as ice dispensers—both manual fill 

and integrated systems. Due to the aggregation of the NAFEM data, it provided additional data 

for putting total shipments into context, but not for identifying the relative shares of shipments 

by equipment class. Shipment estimates have been withheld from publication because the 

NAFEM reports are not publicly available. 

3-15
3.2.5.3 Census Bureau Data

The U.S. Census Bureau’s Current Industrial Report (CIR) series publishes statistics on 

the quantity and value of shipments based on a survey of manufacturers. The CIR has been 

published since 1904, and DOE collected CIR data going back to the 1940s for purposes of 

evaluating prices and shipments. The Census has not updated its statistics since the publication 

of the preliminary analysis, and the CIR has been discontinued.

Table 3.2.9 shows CIR shipment data for ice-making machines.

Table 3.2.9 Shipments of Ice Makers

Product Description Year Quantity Value Companies

Self-contained ice-cube makers, automatic, under 200 

lb

2010 42,293 43,039 4

2009 61,699 55,522

Self-contained ice-cube makers, automatic, 200 lb and 

over

2010 (not reported) 102,696 5

2009 (not reported) 84,524

Self-contained flake or chip machines, 300 lb and 

under

2010 (not reported) (not reported) 3

2009 546 1,369

Self-contained flake or chip machines, over 300 lb 2010 21,368 50,728 6

2009 17,958 42,016

Ice-making machines, not self-contained 2010 107,723 258,408 7

2009 94,876 217,791

Ice-making machines, combination ice makers and 

ice/drink dispensers

2010 74,635 181,612 5

2009 62,367 150,330

Source: U.S. Census Bureau, Current Industrial Reports, 2010. 

www.census.gov/manufacturing/cir/historical_data/ma333m/index.html

Automatic commercial ice makers are treated in U.S. Census Bureau data series as part of 

NAICS 333415. In general, automatic commercial ice makers are such a small piece of NAICS 

333415 that the U.S. Census Bureau does not report them separately in any table, except for the 

CIR tables.

In summary, although the U.S. Census Bureau data contain some limited shipments data 

that would be useful for conducting technical analyses, not enough detail is available to provide 

specific assessments for shipments within each of the primary categories covered in this 

rulemaking.

3.2.5.4 ENERGY STAR Data

ENERGY STAR publishes results annually showing the number of ENERGY STAR 

compliant units shipped by its partners, and the estimate percentage of total shipments 

represented by the compliant shipments. The ENERGY STAR shipments presented on Table 

3.11 covers only equipment types with ENERGY STAR criteria, which during the period 

covered by the shipments data included only air-cooled batch equipment.

3-16
Table 3.2.10 ENERGY STAR Ice Maker Shipments

Year ENERGY STAR

Compliant Shipments

Percent of Total 

Shipments

Total

Shipments

2008 55,274 40% 138,185

2009 60,000 42% 143,000

2010 70,000 63% 111,000

2011 75,000 62% 121,000

2012 95,000 66% 144,000

Source: ENERGY STAR, Unit Shipment Data. 

http://www.energystar.gov/index.cfm?c=partners.unit_shipment_data

3.2.6 Equipment Lifetimes

DOE reviewed available literature and consulted with experts on automatic commercial 

ice makers to establish typical equipment lifetimes. The literature and individuals consulted 

estimated a fairly narrow and consistent range of typical equipment lifetimes, shown in Table 

3.2.11. Most references cited in the table appear to have derived the underlying data for their 

estimates of equipment lifetime from the 1996 Arthur D. Little report.

17

Individuals with experience in the manufacture or distribution of automatic commercial 

ice makers suggested a typical lifetime of 7 to 10 years, with some equipment being retired after 

as short a period as 5 years in cases of building remodels and as long as 20 years in cases where 

the equipment owners did not care about the appearance of the equipment. 

Some literature suggested lifetimes of up to 20 years or more for tube type automatic 

commercial ice makers (tube type ice makers are described in Section 3.3.1.2).

Table 3.2.11 Estimates for Automatic Commercial Ice Maker Lifetimes

Life Reference

7 to 10 years Arthur D. Little, 199617

8.5 years California Energy Commission, 200418

8.5 years Fernstrom, G., 200419

8.5 years Koeller J., and H. Hoffman, 20082

7 to 10 years Navigant Consulting, Inc. 200920

3.3 TECHNOLOGY ASSESSMENT

This section provides a technology assessment for automatic commercial ice makers.

Contained in this technology assessment are details about product operations and components 

(section 3.3.1), an examination of possible technological improvements for each product 

(section 3.3.2), and a characterization of the product efficiency levels currently available on the 

market (section 3.3.3).

3.3.1 Baseline Equipment Components and Operation 

This section briefly describes the components and operation of automatic commercial ice 

makers. These descriptions provide a basis for understanding the technologies used to improve 

product efficiency.

3-17
3.3.1.1 Basic Equipment Description and Components

A typical ice maker consists of a refrigeration system and a water supply system 

contained within an insulated case. The ice makers may or may not have an integrated storage 

bin. Ice makers that have no integral storage bin generally deliver ice by gravity (i.e., dropping 

the ice) into a bin or other equipment, such as a beverage/ice dispenser, on which the ice maker 

is mounted when in use. The refrigeration system may be entirely contained in a single unit, or 

the condenser and possibly the compressor may be located remotely. 

Ice makers are classified into three categories, as discussed in section 3.2.4: ice-making 

heads (IMHs), remote condensing units (RCUs), and self-contained units (SCUs). IMHs have the 

highest sales levels and are available in the widest range of capacities. They are generally 

mounted on top of a separate storage bin or other equipment that uses the ice. The indoor 

sections of RCUs are similar to IMHs, except that they are mated with remote condensers 

intended to be located outdoors, rejecting heat directly to the outside air without adding to the 

interior air-conditioning load. SCUs have their own integral ice storage bins. They generally 

have smaller harvest capacities than IMHs and RCUs, and most of these units are designed for 

under-counter placement.

There are two distinct ice-making processes that are used by commercial ice makers:

• The batch process involves alternate freezing and harvesting periods, producing 

batches of solid ice by allowing water to flow over an evaporator and freeze into 

cubes, tubes, or another shape depending on the evaporator geometry. Once the ice is 

fully formed, the refrigeration system switches into harvest mode, in which 

compressor heat is transferred to the evaporator to release the ice, which is 

subsequently moved to storage.

• The continuous process makes flake and nugget-shaped ice by continuously scraping 

ice as it freezes on a cylindrical evaporator surface. Most commercial flake and 

nugget ice makers use a stationary evaporator that freezes incoming water on the 

enclosed inner surface. These evaporators contain a rotating auger that scrapes the ice 

off the evaporator and extrudes it upward. The auger forces the ice through exit 

orifices past a cutter, whose specific geometry dictates the ice format: in nugget ice 

makers, the flakes are converted into nuggets using an extrusion head with smaller 

openings that compresses the flakes to form nuggets. After passing through the entire 

evaporator, the flakes or nuggets typically drop down into the ice bin.

All commercially available ice makers use vapor compression refrigeration systems to 

provide the refrigeration needed for ice production. Table 3.3.1 provides descriptions for the key 

refrigeration components. Aside from the evaporator, ice makers use the same refrigeration 

components that are used in other medium-temperature commercial refrigeration applications. 

Ice makers use either air-cooled or water-cooled condensers. Air-cooled condensers are used in 

roughly 90 percent of units shipped.

21 Water-cooled condensers can increase energy efficiency 

because the lower inlet water temperature and higher heat transfer possible with water cooling 

allow condensing temperature to be reduced. However, in traditional installations this results in 

much higher water consumption because condenser water is most often drained after it is used to 

cool the condenser. Water-cooled ice makers can, however, be used with closed-water-loop 

3-18
systems in which the condenser heat is remotely rejected in a cooling tower or other equipment 

used to cool the water. Sections 3.3.1.2 (Batch Process) and 3.3.1.3(Continuous Process) 

describe the batch and continuous ice-making processes in more detail.

Table 3.3.1 Brief Description of Primary Refrigeration System Components

Component Description of Typical Unit

Compressor 

Compressor types used:

• Reciprocating compressors for most ice makers

• Scroll compressor for some higher-capacity ice makers (~1,500 lb/24 hours and 

higher)

Refrigerants used:

• R-404A for most icemakers (> 100 lb/24 hours)

• R-134a for some low capacity ice makers (< 100 lb/24 hours) 

Condenser 

Air-Cooled • Copper tube-aluminum fin condensers, generally with enhanced fins (wavy or 

slit fin), maximum fin density near 15 fins per inch.

WaterCooled

• Concentric tube heat exchanger with steel outer and copper or cupronickel inner 

tube, possibly enhanced surface area using spiral grooving. Water flow 

controlled by the supply valve to maintain a constant preset condensing

temperature.

Expansion Device • Most ice makers use conventional thermostatic expansion valves. Electronic 

valves are rarely used. Smaller-capacity ice makers may use capillary tubes.

Evaporator • Copper tubing attached to copper or stainless steel ice-making surfaces (see 

specific descriptions of batch and continuous evaporators below).

Liquid Line/Suction Line 

Heat Exchanger • Liquid and suction lines brazed together.

3.3.1.2 Batch Process

This section describes the batch ice-making process, including the refrigeration cycle, 

evaporator designs, and water supply systems currently used for batch ice-making in the ice 

maker industry. Cube- and tube-shaped ice types are made in batch process ice machines.

Figure 3.3.1 shows key components of a generic refrigeration cycle used for batch icemaking. The system adds to the basic vapor compression cycle a hot-gas bypass line that enables 

the evaporator to undergo a harvest or defrost cycle using hot gas directly from the compressor 

discharge line. The defrost cycle is used to melt the ice layers that hold the ice on the evaporator 

surface, causing the ice to slide off the evaporator. The hot gas solenoid valve opens during 

harvest to let the discharge gas bypass the condenser and thermostatic expansion valve to pass 

directly into the evaporator.

3-19
Source: Manitowoc

Figure 3.3.1 Typical Vapor Compression Refrigeration Cycle for Batch Type Ice Makers

There are five basic evaporator designs used by manufacturers in the ice maker industry 

today for ice makers with harvest capacities in the commercial range (up to 4,000 lb/24 hours). 

These designs are described in Table 3.3.2. The cube type evaporator consists of copper or 

stainless steel ice-making surfaces brazed to copper serpentine tubing, enabling the exchange of 

heat between the water and the refrigerant. The tube and cracked ice evaporators are used for ice 

makers with harvest capacities of 2,000 lb/24 hours and higher. Figure 3.3.2 shows cracked ice 

during harvest, illustrating the cracks that form due to thermal stresses associated with harvest 

heat addition just prior to ice release. 

3-20
Table 3.3.2 Batch Evaporator Designs

Evaporator 

Design

Manufacturers 

Using Design Ice Shape Description

Vertical Grid

Manitowoc, 

Scotsman 

Industries

Rectangular 

Cube

Nickel-plated copper grid oriented vertically with 

copper serpentine tubing brazed onto rear plate; water 

flows down the face of the grid and freezes in cubeshaped cavities to form ice cubes.

Hoshizaki 

Style Hoshizaki

Crescent Cube Vertically oriented pair of stainless steel surfaces 

shaped to ensure formation of individual crescent 

cubes and sandwiching the copper serpentine 

evaporator tube; water flows down both sides. 

Horizontal 

Grid Kold-Draft

Rectangular 

Cube

Nickel-plated copper grid oriented horizontally with 

the ice-making surface facing downward with copper 

serpentine tubing brazed on the top; water is sprayed 

from below onto the ice-making surface by multiple 

nozzles.

Tube Vogt, Others

Cylindrical 

Tube

Vertical cylindrical stainless steel drum housing an 

array of vertical stainless steel tubes; water flows 

down and freezes on the inside surface of the tubes, 

forming long ice tubes that are cut into smaller pieces 

as they fall through and out the tubes during harvest; 

refrigerant flows inside the drum outside the tubes.

Cracked Vogt, Arctic 

Temp, Others Cracked Pieces

One or more vertically oriented concentric-cylindrical 

stainless steel tube evaporators with refrigerant 

flowing between the cylinders; water flows down and 

ice forms on the inner and outer surfaces. The thermal 

stress of harvest starts to crack the cylindrical ice, and 

it is ground into smaller pieces mechanically.

Image sources:

Vertical Grid – Manitowoc: www.manitowocice.com

Hoshizaki Style: www.ccfse.com/Hoshizaki-Crescent-Ice-Cuber-p/km-650mah.htm

Horizontal Grid – Kold-Draft: www.kold-draft.com/ice-making-technology/ice-sizes.php

Tube Ice: www.prithvinigen.com/tube_ice.jpg

3-21
Source: Vogt Ice (www.vogtice.com/downloads/General%20Info-VT.pdf)

Figure 3.3.2 Cracked Ice Forming on “Cracked Ice” Evaporators

Figure 3.3.3 shows the typical water system for cube type ice makers using vertically

oriented evaporators.

3-22
Figure 3.3.3 Water System Diagram for Batch Type Ice Makers

Batches of ice are produced through a series of steps, as described below:

1. The sump fills with water. Some ice maker designs fill the sump completely before 

beginning the ice-making process, while others allow the filling process to continue 

through the ice-making process. The continuous fill technique is common in selfcontained units.

2. Water is circulated over the evaporator, and ice gradually forms. Water that does not 

freeze on the evaporator falls to the sump to be recirculated by the pump. Solids and 

gases dissolved in the water are carried away by the remaining liquid. This process 

reduces inclusion of impurities in the ice, thus allowing production of clear ice. 

3. When the batch is complete, ice is harvested using hot refrigerant vapor that has been 

redirected from the compressor discharge to warm the evaporator to free the ice from 

the surface. The harvested ice falls into a storage bin, which may or may not be part 

of the ice maker. The ice maker can sense when the ice has reached the proper batch 

weight by (1) measuring sump water level; (2) waiting a certain amount of time after 

the compressor suction pressure drops to a preset level; or (3) measuring thickness of 

ice on the plate.

4. The remaining sump liquid, which contains high concentrations of dissolved solids, is 

drained. Depending on ice maker design details, the liquid may or may not be 

completely drained, and there may or may not be fresh potable water flowing during 

Water 

Pump

Sump

Water Distributor

Water 

Supply

Purge Drain 

3-23
this time to help purge the contaminants. Typical potable water consumption ranges 

from 18–40 gallons for every 100 lb of ice produced, compared to 12 gallons per 

100 lb contained in the ice produced.

5. After completion of draining, the sump is refilled.

Manufacturers of cube ice machines have been able to minimize meltage during harvest 

using mechanical or pressurized-air assist. Such methods rely on the formation of an ice bridge 

between cubes to transfer the force between cubes and/or ensure that the pressurized air spreads 

out over the evaporator surface. Hoshizaki ice makers utilize the incoming potable water stream 

to assist in the harvest process by directing the incoming water behind the two evaporator plates. 

The water can provide a substantial amount of the heat required for harvest. This approach also 

pre-chills the incoming water for the next batch of ice. Harvest times range from less than 

1 minute up to 2 minutes.

Tube and cracked ice machines use a cutter at the base of the evaporator to chop the ice 

into small pieces as it falls. 

3.3.1.3 Continuous Process

This section describes the “continuous” ice-making process, including the refrigeration 

cycle, the evaporator designs, and the water supply systems currently used for continuous icemaking in the ice maker industry. As previously stated, the continuous process is used to make 

flake and nugget ice.

Figure 3.3.4 shows the typical refrigeration cycle used for continuous ice-making. The

cycle involves the basic vapor compression cycle. In contrast to the batch process, there is no 

hot-gas bypass line, because there is no need for a harvest cycle.

3-24
Source: Manitowoc

Figure 3.3.4 Typical Vapor Compression Refrigeration Cycle for Continuous Ice Makers

The continuous ice maker evaporator is typically a stainless steel cylinder surrounding an 

auger that moves ice upward toward an extruding head, as shown in Figure 3.3.5. There are 

currently two variations of this design used across the industry. The most common design 

involves wrapping the copper refrigerant tube around the cylinder and brazing the assembly to 

allow heat to be exchanged between the water and the refrigerant. A variation of this design uses 

concentric stainless steel cylinders to form a spiral-shaped space for refrigerant flow, as shown in

Figure 3.3.5(B). The auger drives the water and ice upward as the water freezes on the cold inner 

surface of the evaporator cylinder. The ice is pushed through an extrusion head. The extrusion 

head for a nugget ice maker has smaller holes than that of a flake ice maker, causing the flakes to 

be compressed into nuggets as they leave the evaporator. Some designs use a rotating cutter to 

help shape the ice. At this point the ice is directed to the storage bin, often via gravity drop 

through a vertical transfer tube. Flake- and nugget-type ice makers do not have a purging process 

to remove impurities, and the resulting ice retains the impurities, similar to ice production in 

residential refrigerators. However, some designs use an occasional flush to remove impurities 

that may remain in the evaporator.

Compressor

Air or Water Condenser

Evaporator

Thermostatic 

Expansion Valve

Liquid-line 

Filter Drier

Suction-Line 

Heat Exchanger

3-25
(A) Copper-tube continuous evaporator and auger motor (Ice-O-Matic)

(B) Stainless-steel continuous evaporator (Follett)

Sources: 

www.kirbysupply.com/Equipment/Ice_Machines/IOM_Flaker_Self_Contained_fp.htm

www.follettice.com/images/image_bank/AAI_Evaporator.jpg

Figure 3.3.5 Cross-Section of Continuous Evaporator and Auger

An alternative flaker system commercialized by Howe Corporation for capacities of 

1,000 to 40,000 lb/24 hours uses an ice blade attached to a rotating shaft to score and wedge the 

ice off the inside surface of the evaporator drum. In this design, the ice is sub-cooled so that it is 

22 °F when harvested, and it gravity-drops to a storage bin. 

3.3.2 Technology Options

The following sections discuss technology options for energy use reduction in ice 

makers. DOE’s primary focus in developing its analysis for this rulemaking is the reduction of 

energy use. Reduction of potable water use and condenser water use are considered in the 

context of their relationship to energy use. Hence, the technology options do not include 

technology options associated with water use reduction. 

Table 3.3.3 lists the technology options DOE identified for improving the efficiency of 

automatic commercial ice makers. The technology options are categorized by their associated 

component or system. Each technology option category and the options available for improving 

the component or system category are discussed below.

3-26
Table 3.3.3 Technology Options for Automatic Commercial Ice Makers

Technology Options Batch

Ice Makers

Continuous 

Ice Makers Notes

Compressor

Improved compressor 

efficiency √ √

Part-load operation √ √

Condenser

Increased surface area √ √

Enhanced fin surfaces √ √ Air-cooled only

Increased air flow √ √ Air-cooled only

Increased water flow √ √ Water-cooled only

Brazed plate condenser √ √ Water-cooled only

Microchannel condenser √ √ Air-cooled only

Fans and Fan 

Motor

Higher efficiency condenser 

fans and fan motors √ √ Air-cooled only

Other Motors 

Improved auger motor 

efficiency √ Continuous only

Improved pump motor 

efficiency √ Batch only

Controls Smart Technologies √ √

Evaporator

Design options which reduce 

energy loss due to evaporator 

thermal cycling

√ Batch only

Design options which reduce 

harvest meltage or reduce 

harvest time

√ Batch only

Increased surface area √ √

Insulation

Improved insulating material 

and/or thicker insulation around 

the evaporator compartment

√ √

Refrigeration 

Line Larger diameter suction line √ √ Primarily remote 

compressor equipment

Potable Water Reduced potable water flow √ Batch only

Drain water thermal exchange √ Batch only

3.3.2.1 Compressor

The compressor is the primary energy consuming component in an ice maker. Most ice 

makers use hermetic compressors in which the entire motor-compressor assembly is hermetically 

sealed in the welded steel shell. Hermetic reciprocating compressors are the most commonly 

used compressors in ice makers, although hermetic scroll compressors and semi-hermetic 

reciprocating compressors are also used for the highest capacity equipment. Semi-hermetic 

compressors can be opened for repair of internal components—they are typically very heavy, 

because the flanges or sealed plates required for access must be thick and stiff to prevent 

refrigerant leaks during operation.

Almost all compressors are directly driven by two-pole, squirrel-cage induction motors 

running at approximately 3,000 rpm on 60 Hz power. Four types of single-phase induction 

motors have been used in ice maker compressors: capacitor start/capacitor run (CSCR), 

3-27
resistance start/induction run (RSIR), capacitor start/induction run (CSIR), and resistance 

start/capacitor run (RSCR). Of the four motor types, the RSIR motor is the least efficient. Singlephase compressors in ice makers run on either 115 or 230 V power. Larger ice makers can also 

use three-phase motors.

Ice maker compressor capacities range from approximately 1,500 British thermal units 

(Btu) per hour to 40,000 Btu/hr. Three organizations have established conditions for rating the 

performance of compressors: Air-conditioning, Heating, and Refrigeration Institute (AHRI), 

American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE), and 

Comité Européen des Constructeurs de Matériel Frigorifique (CECOMAF).c Table 3.3.4 shows 

the rating conditions of these organizations. The performance ratings are based on the premise 

that refrigerant tubing connecting system components are adiabatic (i.e., that they undergo no 

thermal exchange with the environment) and that they don’t impose refrigerant pressure drop. 

Note that the actual operating conditions for compressors in ice makers under DOE energy test 

conditions can be different than these rating conditions. In fact, batch ice maker compressors 

operate over a range of conditions during each ice-making cycle.

Table 3.3.4 Compressor Rating Conditions (oF (oC))

Rating Condition ARI 540 MBP 

Type 1

ARI 540 MBP

Type 2

ASHRAE MBP CECOMAF M

Evaporating 

Temperature* 20 (-6.7) 20 (-6.7) 20 (-6.7) 14 (-10)

Condensing 

Temperature** 120 (48.9) 120 (48.9) 130 (54.4) 131 (55)

Suction Temperature 65 (18.3) 40 (4.4) 95 (35) 90 (32)

Condenser Outlet 

Temperature / Liquid 

Temp†

115 (46.1)

Subcooling† 0 (0) 0 (0) 0 (0)

MBP = medium back pressure *

Refrigerant dew point temperature corresponding to suction pressure. **Refrigerant dew point temperature corresponding to discharge pressure. †

Temperature difference between refrigerant bubble point temperature corresponding to discharge pressure and 

temperature at condenser outlet.

DOE updated its compressor performance data during the NOPR analyses, based on 

feedback from stakeholders. Additional information on the compressor data can be found in 

chapter 5 of the final rule TSD.

Compressor efficiency is generally expressed as an energy efficiency ratio (EER)—a 

ratio of refrigeration capacity to input power with units of Btu/watt-hour. The EER of a 

compressor depends on the compressor operating conditions. Also, the peak EERs of 

compressors vary significantly over the range of compressor capacities used in ice makers, with

higher EERs achieved by higher capacity compressors. 

c CECOMAF is a European appliance manufacturer trade association formed in 1958. It merged with EUROVENT 

in 1996 to become EUROVENT/CECOMAF. This organization is now called EUROVENT.

3-28

 
Improved Compressor Efficiency

Conversion to high-efficiency compressors is fairly straightforward for manufacturers to 

implement as long as compressors of a suitable capacity are available and they do not present 

additional challenges such as larger physical size or greater noise levels. The potential for 

efficiency improvement through use of higher efficiency compressors can be significant, but it 

depends on the specific circumstances for a given, existing ice maker design. Chapter 5 discusses 

in greater depth the potential for improving efficiency through use of higher EER compressors, 

including the updated compressor EER levels DOE applied to the final rule analysis.

Part-Load Operation

Part-load operation may be achieved using variable-speed, variable-capacity, or dualcapacity compressors, or by using two compressors. This option would reduce the energy used 

when the ice maker is producing ice at less than its full harvest capacity. During part-load 

operation, the temperature differentials between the evaporating refrigerant and the freezing 

water and also between the condensing refrigerant and the cooling medium can be reduced, thus 

improving the compressor’s operating EER. Information demonstrating such approaches in ice 

makers has not been made public, but variable-speed compressors are being used in other 

refrigeration applications, such as residential refrigerators. 

3.3.2.2 Condenser

Increased Surface Area

Increasing the heat transfer surface area of air-cooled condensers can be achieved by 

(a) increasing the width of the rows of tubes; (b) adding more tube rows along the direction of air 

flow; or (c) adding more tube rows across the direction of air flow. These measures can be 

limited by the geometry of the ice maker. Increasing tube width can also result in an increased 

refrigerant pressure drop across the condenser, so there is a tradeoff between improving heat 

transfer to reduce condensing temperature and increasing condenser inlet pressure due to the 

higher pressure drop. Similarly, adding tube rows can increase the air pressure drop through the 

condenser, so there is a tradeoff between improving heat transfer and increasing fan work.

The surface area of water-cooled ice makers can also be increased through the use of 

larger coaxial condenser designs.

Increasing condenser size also has implications associated with limits on equipment size. 

Increasing the size of an ice maker to allow for a condenser size increase may not be acceptable 

because many ice makers have standard sizes. Such limitations may be less important for remote 

condensers, however. There is often greater potential for increase in the size of condensers of 

water-cooled ice makers, since water-cooled and air-cooled designs are often based on the same 

platform, and the air-cooled condensers, condenser fan/motor assemblies, and space for air flow 

generally take up much more space than water-cooled condensers.

One issue to consider when implementing condenser size increase is the added refrigerant 

charge associated with such a design change. The added refrigerant can add cost, but also can 

increase the challenges associated with refrigerant management throughout both the freeze and 

3-29
harvest cycles of batch ice makers. While few IMH-style batch ice makers have refrigerant 

receivers, an increase of condenser size can potentially lead to required use of receivers to ensure 

reliable operation and avoid damage such as would occur with flooding of the compressor.

Enhanced Fin Surfaces (Air-Cooled Models)

Enhanced fin surfaces can improve the performance of air-cooled condensers by reducing

air-side thermal resistance. Several such surfaces to improve performance have become available 

for use in fluid/air heat exchangers over the years. Among the most common types of fin surface 

enhancements are wavy fins and slit fins. Most air-cooled ice makers already take advantage of 

such enhanced fin surfaces. 

Increased Air Flow (Air-Cooled Models)

Increasing the air flow rate through the condenser will increase the heat removal rate and 

reduce condensing temperature. However, increasing air flow generally requires increased fan 

input power, so there are limits to the benefits of this approach. Fan size increase, if necessary to 

increase air flow rate, can also be limited by the geometry of the ice maker, making it difficult to 

increase air flow in some models.

Increased Condenser Water Flow (Water-Cooled Models)

Increasing the condenser water flow rate for water-cooled ice makers will improve heat 

transfer. However, this measure can significantly increase costs associated with water 

consumption in installations where condenser water is drained to a building’s waste water 

system. Cube ice makers are covered by the current DOE standards, which limit condenser water 

use. During the preliminary analysis, DOE provisionally determined that backsliding on 

condenser water standards to improve energy efficiency is not prohibited, but that such a change 

must consider the cost effectiveness of increased condenser water flow.

Brazed Plate Condenser (Water-Cooled Models)

Brazed plate water-cooled condensers can have much larger heat transfer areas for a 

given volume than the concentric-tube condensers used in most ice makers.

Microchannel Condenser

Unlike a conventional coil tube with an attached plate fin, microchannel heat exchangers 

have a rectangular aluminum cross section containing several small channels (typically less than 

1mm across) through which refrigerant passes. Aluminum fins are brazed between the 

rectangular tubes. Microchannel can increase heat transfer while reducing airside pressure drop 

as compared with conventional coils. Microchannel heat exchangers generally weigh less and 

hold significantly less refrigerant than conventional heat exchangers.

3-30
3.3.2.3 Higher Efficiency Condenser Fans and Fan Motors 

Fans are used to draw or blow air through air-cooled condensers. Condenser fans used in 

ice makers almost exclusively are axial design. 

One source of inefficiency for axial fans lies in their tendency to throw air outward. The 

Pax GroupTM has developed a fan (PAX fan) that employs streamlined blades with patented 

geometrical shapes that reportedly provide better air flow direction and improved efficiency. 

Though a prototype has not yet been developed for ice makers, Pax is working to optimize the 

fans for other commercial refrigeration equipment, including supermarket display cases and 

vending machines. Tests performed with the PAX fan have demonstrated a reduction in fanmotor power of 15 to 20 percent.22 At this point, because the PAX fan is proprietary, the 

widespread use of the design is highly uncertain.

Most condenser fan motors used in products today have shaded pole induction designs 

with efficiencies between 20 and 35 percent, or permanent split capacitor (PSC) designs, which 

reach efficiencies of 40 to 65 percent. Brushless DC motors can have efficiencies up to 80 

percent, thus representing an option for further efficiency improvement.

3.3.2.4 Improved Auger Motor Efficiency

Flake and nugget machines use an auger motor to drive the auger. CSIR, CSCR, and PSC 

motors are generally used in single-phase designs. Higher efficiency permanent magnet motors 

could also be used for auger motors. 

3.3.2.5 Improved Pump Motor Efficiency

Batch machines use a pump to transfer water from the sump to the top of the evaporator. 

Most ice maker pumps use shaded-pole motors. However, PSC or brushless-DC motors could 

also be used with ice maker pumps.

3.3.2.6 Demand Response Controls

Ice makers can be integrated into communication infrastructures that allow electric utility 

controllers to broadcast demand response events to customer electric meters, which then 

communicate with the target appliance. The ice maker must be configured with appropriate 

hardware and software to enable two-way communication. According to demand response 

studiesd

, this connectivity can be achieved using an electronic board for communications and an 

ultrasonic sensor to read the ice level in the ice maker’s storage bin. The ice maker would then 

be able to communicate with a smart meter and limit ice production during a demand response 

event. While this capability would alleviate peak loads experienced by electric utilities, it would 

likely not result in energy savings.

d “Demand Response Strategies Using Two-Way Connectivity For Commercial Ice Machines.” DR 07.07 Report. 

March 2009. Southern California Edison.

3-31

 
3.3.2.7 Evaporator

Manufacturers generally fabricate a limited number of evaporator sizes, which are used 

across the product lines matched to the extent possible with the appropriately sized compressors. 

This manufacturing strategy contributes to variations of energy efficiency across the product line 

because the evaporator/compressor combination cannot be optimized for each ice maker, 

resulting in some ice makers with undersized evaporators having oversized compressors to 

achieve the target production rate. Such products may have correspondingly higher energy 

consumption.23 This generally holds true for both batch and continuous ice makers.

Batch ice maker evaporator design requires finding a careful balance between the ice 

growth behavior, water flow rate over the evaporator, localized water distribution, materials 

selection, and harvest performance (e.g., successful ice detachment with little meltage). 

Evaporator design is a complex process not amenable to analysis, and developing a successful 

evaporator design requires many hours of laboratory testing. Manufacturers are very reluctant to 

change the evaporator design once a successful design has been developed.

Increased Evaporator Size

A larger evaporator size would allow for a higher evaporating temperature, thus allowing 

the compressor to operate more efficiently. It is important to note that increasing evaporator size 

has implications associated with limits on equipment size. Increasing the size of an ice maker to 

allow for an evaporator size increase may not be acceptable because many ice makers have 

standard sizes. 

Design Options that Reduce Energy Loss due to Evaporator Thermal Cycling

Energy loss associated with thermal cycling depends on the thermal mass of the portions 

of the evaporator that must cool down during the freeze cycle and warm up during harvest, and 

with the temperature extremes of the thermal cycling. These losses can be reduced by reducing 

thermal mass and moderating the temperature extremes. Hence, both thermal mass and thermal 

resistance between the evaporator and the ice must be considered in alternative designs to reduce 

this loss. Numerous patents suggest evaporator designs with reduced thermal mass or otherwise 

improved performance. However, performance data is publicly available for few of these 

designs, perhaps just for one: the evaporator design used in most Hoshizaki cube ice machines. 

DOE requested information regarding proprietary status of low-thermal-mass evaporator designs 

during the preliminary analysis phase. Chapter 4 of the final rule TSD discusses DOE’s findings 

on the proprietary status of this technology.

Design Options that Reduce Harvest Meltage or Reduce Harvest Time

Reduction of harvest meltage will improve efficiency by preserving more ice for harvest 

and by reducing harvest time. The reduction of harvest time improves efficiency by increasing 

harvest capacity because it reduces total cycle time associated with a given batch weight. Harvest 

meltage has been reduced in cube ice makers by ensuring smooth evaporator surfaces, allowing 

buildup of ice bridges connecting the cubes, and employing harvest assist. Ensuring that surfaces 

in contact with the ice are smooth prevents holdup of the cubes as they slide out of the 

evaporator cube cells. Ice bridges connecting cubes allow the entire ice sheet to be released at 

3-32
once, which can help ensure harvest of individual cubes that might be held up as they release 

from the evaporator. Harvest assist involves active removal of the ice sheet from the evaporator. 

This is done in current ice maker designs using a mechanical method employing a rod that 

pushes on the ice sheet from the evaporator side24 (this method is used by Scotsman and Ice-OMatic ice makers) or by blowing air behind the ice sheet from the rear of the evaporator to create 

pressure to remove the ice25 (this method is used by Manitowoc ice makers). The speed of 

harvest and the meltage rate are also affected by details of refrigerant compressor design, control 

of evaporator pressure and refrigerant flow during harvest, whether the ice maker has a remote 

condenser and/or compressor, and the use of incoming potable water to help in warming the ice. 

Additional design adjustments may be possible to further improve harvest to reduce harvest 

meltage or reduce harvest time, but information is not publicly available detailing such additional 

options and/or their effectiveness.

3.3.2.8 Improved or Thicker Insulation

Insulation can be used around the evaporator compartment and the sump to minimize the 

thermal load through the walls of these parts of the ice maker. Ice maker manufacturers use 

varying amounts of insulation. Some manufacturers simply place a piece of batt insulation 

between the ice-making and compressor compartments, while other manufacturers use 

polystyrene pieces or blown-in polyurethane foam in the walls and/or base of the unit. Increased 

thickness and improved resistivity are both options to reduce the thermal load on the 

refrigeration system, reducing energy consumption. 

3.3.2.9 Larger Diameter Suction Line (Remote Compressor Models)

Remote condenser units must be tested with at least 25 feet of interconnecting refrigerant 

piping between the condensing unit and the ice-making unit (AHRI Standard 810-2007, section 

4.1.3). For ice makers with both remote condensers and remote compressors, this significantly 

increases the length of the suction line, resulting in additional pressure drop between the 

evaporator and the compressor suction inlet. Increasing the size of the suction line would reduce 

this pressure drop. However, consideration must be given to oil return, which can depend on 

maintaining relatively high suction velocities. If the suction line refrigerant velocity is too low, 

oil may not return reliably to the compressor, thus increasing the risk of oil loss and compressor 

failure.

3.3.2.10 Reduced Potable Water Flow

Potable water use and energy use of ice makers are related. Some or all of the purge 

water drained from batch ice makers leaves the equipment near 32 °F. This represents lost 

refrigeration that could potentially have been used to produce more ice. However, water is 

purged from batch ice makers to remove dissolved solids that enter with the potable water 

supply. Selecting excessively low potable water levels can lead to increased maintenance cost 

associated with an increased need for descaling operations, and, after the ice maker has operated 

for a number of cycles, the scale build-up can reduce ice production and increase energy use.

3-33
3.3.2.11 Drain Water Thermal Exchange

Batch ice makers can benefit from drain water thermal exchange that cools potable water 

supply entering the sump, thereby reducing the energy required to freeze the water. In most batch 

ice makers, the sump pump circulates water over the evaporator throughout the freeze cycle, and 

only drains and refills during the harvest. At the end of the freeze cycle, the water in the sump is 

just above freezing temperature, so this water could be drained to a secondary sump or reservoir.

The water inlet tube would pass through this secondary sump to facilitate thermal exchange 

between the water entering the sump at supply water temperature and the recently drained water 

near freezing temperature. Efficiency gains are limited by the fact that purge water amounts have 

been reduced to about one-third of the supply water quantity for many ice makers. 

During the NOPR phase of the rulemaking, DOE performed testing of a drain water heat 

exchanger with an ice maker to assess its performance. The results of this testing are described in 

Chapter 5 of the final rule TSD.

3.3.3 Energy Use Data

DOE gathered data on the energy use of automatic commercial ice makers currently 

available in the marketplace. DOE created a database of the current models by surveying 

manufacturers’ websites. The data provide an overview of the energy use of each equipment 

class covered by this rulemaking according to the current DOE test procedure, which references 

AHRI Standard 810-2007. Figure 3.3.6 through Figure 3.3.15 show energy consumption as a 

function of harvest capacity within each equipment class. The current DOE standards and 

ENERGY STAR criteria (when applicable), as well as the trial standard levels (TSLs) DOE 

considered in the rulemaking, are shown in the figures. Where appropriate, the figures show 

separately the ratings for units with proprietary technology (low-thermal mass or tube 

evaporators), and the ratings for units that are 22 inches wide. The “TSL3 rep units” show the 

TSL3 efficiency levels for the capacities and classes that DOE directly analyzed.

For continuous ice makers, the energy use shown in these plots has been adjusted based 

on ice hardness, as required by the DOE test procedure. For ice makers for which ice hardness 

data is not available, DOE assumed that ice hardness is 0.7 for flake ice makers and 0.85 for 

nugget ice makers. The figures showing energy use data for flake and nugget machines indicate 

the baseline efficiency levels that DOE adopted for the rulemaking analyses, since there are 

currently no DOE standards for this equipment. 

3-34
Figure 3.3.6 Energy Consumption vs. Harvest Rate (Air-Cooled Cube Ice-Making Heads)

 -

 2

 4

 6

 8

 10

 12

0 200 400 600 800 1000 1200 1400 1600 1800

Energy Consumption (kWh/100 lbs ice)

Harvest Rate (lbs ice / 24 hours)

Current Standard Current Market 22 Inch Wide

TSL3 Rep Units TSL1 TSL4

TSL5 Max Tech TSL3 Max NPV 7% Proprietary Technology

Power (ENERGY STAR)

3-35
Figure 3.3.7 Energy Consumption vs. Harvest Rate (Water-Cooled Batch Ice-Making 

Heads)

 -

 1.00

 2.00

 3.00

 4.00

 5.00

 6.00

 7.00

 8.00

 9.00

0 500 1000 1500 2000 2500 3000

Energy Consumption (kWh/100 lbs ice)

Harvest rate (lbs ice / 24 hours)

Current Standard Current Market 22 Inch Wide

TSL3 Rep Units TSL1 TSL4

TSL5 Max Tech TSL2 TSL3 Max NPV 7%

Proprietary Technology

3-36
Figure 3.3.8 Energy Consumption vs. Harvest Rate (Air-Cooled Batch Remote Condensing 

Units [Non-Remote Compressor])

 -

 1

 2

 3

 4

 5

 6

 7

 8

 9

0 500 1000 1500 2000 2500 3000 3500 4000

Energy Consumption (kWh / 100 lbs ice)

Harvest Rate (lbs ice / 24 hours)

Current Standard Current Market 22 Inch Wide ENERGY STAR

TSL3 Rep Units TSL1 TSL4 TSL5 Max Tech

TSL3 Max NPV 7% Proprietary Technology

3-37
Figure 3.3.9 Energy Consumption vs. Harvest Rate (Air-Cooled Batch Remote Condensing 

Units [Remote Compressor])

 -

 1

 2

 3

 4

 5

 6

 7

 8

 9

0 500 1000 1500 2000 2500 3000 3500 4000

Energy Consumption (kWh / 100 lbs ice)

Harvest Rate (lbs ice / 24 hours)

Current Standard Current Market 22 Inch Wide

ENERGY STAR TSL3 Max NPV 7% TSL1

TSL4 TSL5 Max Tech Proprietary Technology

3-38
Figure 3.3.10 Energy Consumption vs. Harvest Rate (Air-Cooled Batch Self-Contained 

Units)

 -

 2

 4

 6

 8

 10

 12

 14

 16

 18

0 50 100 150 200 250 300 350 400 450 500

Energy Consumption (kWh / 100 lbs ice)

Harvest Rate (lbs ice / 24 hours)

Current Standard Current Market ENERGY STAR

TSL3 Rep Units TSL1 TSL4

TSL5 Max Tech TSL3 Max NPV 7% Proprietary Technology

3-39
Figure 3.3.11 Energy Consumption vs. Harvest Rate (Water-Cooled Batch Self-Contained 

Units)

 -

 2

 4

 6

 8

 10

 12

0 50 100 150 200 250 300 350 400 450 500

Energy Consumption (kWh / 100 lbs ice)

Harvest Rate (lbs ice / 24 hours)

Current Standard Current Market TSL3 Rep Unit TSL3 Max NPV 7%

TSL1 TSL4 TSL5 Max Tech

3-40
Figure 3.3.12 Energy Consumption vs. Harvest Rate (Air-Cooled Continuous Ice-Making 

Heads)

Note: Energy consumption in this this figure is adjusted for ice hardness, as specified by the 

DOE Test Procedure.

0

2

4

6

8

10

12

0 500 1000 1500 2000 2500

Energy Consumption (kWh / 100 lbs ice)

Harvest Rate (lbs ice / 24 hours)

Baseline Current Market 22 Inch Units ENERGY STAR

TSL3 Rep Units TSL1 TSL2 TSL3 Max NPV 7%

TSL4 TSL5 Max Tech

3-41
Figure 3.3.13 Energy Consumption vs. Harvest Rate (Water-Cooled Continuous IceMaking Heads)

Note: Energy consumption in this in this figure is adjusted for ice hardness, as specified by the 

DOE Test Procedure. 

0

1

2

3

4

5

6

7

8

9

0 500 1000 1500 2000 2500

Energy Consumption (kWh / 100 lbs ice)

Harvest Rate (lbs ice / 24 hours)

Baseline Current Market 22 Inch Wide

TSL3 Rep Units TSL1 TSL2

TSL3 Max NPV 7% TSL4 TSL5 Max Tech

3-42
Figure 3.3.14 Energy Consumption vs. Harvest Rate (Air-Cooled Continuous Remote 

Condensing Units with Non-Remote Compressors)

Note: Energy consumption in this in this figure is adjusted for ice hardness, as specified by the 

DOE Test Procedure. 

 -

 2

 4

 6

 8

 10

 12

0 500 1000 1500 2000 2500

Energy Consumption (kWh / 100 lbs ice)

Harvest Rate (lbs ice / 24 hours)

Baseline Current Market 22 Inch Wide ENERGY STAR TSL3 Rep Unit

TSL3 Max NPV 7% TSL1 TSL4 TSL5 Max Tech

3-43
Figure 3.3.15: Energy Consumption vs. Harvest Rate (Air-Cooled Continuous Remote 

Condensing Units with Remote Compressors)

Note: Energy consumption in this in this figure is adjusted for ice hardness, as specified by the 

DOE Test Procedure. 

 -

 2

 4

 6

 8

 10

 12

0 500 1000 1500 2000 2500

Energy Consumption (kWh / 100 lbs ice)

Harvest Rate (lbs ice / 24 hours)

Baseline Current Market 22 Inch Wide ENERGY STAR

TSL3 Max NPV 7% TSL1 TSL4 TSL5 Max Tech

3-44
Figure 3.3.16 Energy Consumption vs. Harvest Rate (Air-Cooled Continuous SelfContained Units)

Note: Energy consumption in this figure is adjusted for ice hardness, as specified by the DOE 

Test Procedure. 

0

2

4

6

8

10

12

14

16

0 100 200 300 400 500 600 700 800 900 1000

Energy Consumption (kWh / 100 lbs ice)

Harvest Rate (lbs ice / 24 hours)

Baseline Current Market ENERGY STAR

Max NPV 7% Rep Unit TSL3 Max NPV 7% TSL1

TSL4 TSL5 Max Tech TSL2

3-45
Figure 3.3.17 Energy Consumption vs. Harvest Rate (Water-Cooled Continuous SelfContained Units)

Note: Energy consumption in this figure is adjusted for ice hardness, as specified by the DOE 

Test Procedure. 

3.3.4 Condenser Water Consumption Data

DOE’s updated database of products includes information on condenser water 

consumption for the four equipment classes that are water-cooled: IMH-W-B, SCU-W-B, IMHW-C, SCU-W-C. Figure 3.3.17 through Figure 3.3.20 contain the condenser water consumption 

data for these equipment classes.

For the two batch-type equipment classes, DOE is maintaining the condenser water 

consumption standard at the current level through harvest capacity up to 2,500 lb ice/24 hours, 

and establishing constant standard levels for harvest capacity rates from 2,500 to 4,000 lb ice/24 

hours. 

 -

 1

 2

 3

 4

 5

 6

 7

 8

 9

 10

0 100 200 300 400 500 600 700 800 900 1000

Energy Consumption (kWh / 100 lbs ice)

Harvest Rate (lbs ice / 24 hours)

Baseline Current Market TSL3 Max NPV 7% TSL1 TSL4 TSL5 Max Tech

3-46
Figure 3.3.18: Condenser Water Consumption vs Harvest Rate (Water-Cooled Ice-Making 

Head Units)

3-47
Figure 3.3.19: Condenser Water Consumption vs Harvest Rate (Water-Cooled Batch-Type 

Self-Contained Units)

For the two continuous-type equipment classes, there is no existing standard for condenser water 

consumption. DOE based the condenser water standards for these classes on the respective batch 

standards, but lowered these standards to reflect the existing market for continuous type units. In 

the NOPR, DOE proposed setting continuous ice maker condenser water use standards at 20% 

below those of batch ice makers, citing the reduced heat rejection required for continuous units, 

because continuous ice makers do not have a harvest event that adds to heat rejection. In the final 

rule, DOE has revised the standard for IMH-W continuous ice makers to be 10% below that of 

the IMH-W batch classes, as shown in Figure 3.3.19.

3-48
Figure 3.3.20: Condenser Water Consumption vs Harvest Rate (Water-Cooled Continuous

Ice-Making Head Units)

0

50

100

150

200

250

0 500 1000 1500 2000 2500 3000 3500 4000

Condenser Water Usage (gallons / 100 lb ice)

Harvest Rate (lb ice / 24 hours)

Batch Standard 10% Below Batch Standard Proposed Continuous Standard Market Data

3-49
Figure 3.3.21: Condenser Water Consumption vs Harvest Rate (Water-Cooled Continuous

Self-Contained Units)

The current and new condenser water standard equations are described in Table 3.3.5

below.

Table 3.3.5: Comparison of Current and Proposed Condenser Water Standards

Equipment Class Current Condenser 

Water Standard

New Condenser Water 

Standard

IMH-W-B 200 – 0.022 * H 200 – 0.022 * H

SCU-W-B 191 – 0.0315 * H 191 – 0.0315 * H

IMH-W-C No current standard 180 – 0.0198 * H

SCU-W-C No current standard 153 – 0.0252 * H

3-50
REFERENCES

1. Air-Conditioning, Heating, and Refrigeration Institute. (Last accessed November 30, 

2014.) < http://www.ari.org/site/626/About-Us/Product-Sections/Automatic-CommercialIce-Makers-Ice-Storage-Bins >

2. Koeller J., and H. Hoffman. A Report on Potential Best Management Practices. 2008. 

Prepared by Koeller and Company for the California Urban Water Conservation Council, 

Sacramento, CA.

3. Enodis, PLC. Excerpts from 20-F SEC Filing, filed by ENODIS PLC on 12/19/2003. 

Information found on-line via a Google search, at the following web address (last accessed 

November 30, 2014): <http://sec.edgar-online.com/enodis-plc/20-f-annual-and-transitionreport-foreign-private-issuer/2003/12/19/section6.aspx>

4. Manitowoc Company. The Manitowoc Company signs definitive agreement to divest 

Enodis ice business (press release). March 30, 2009. Manitowoc, WI. 

<www.manitowocfsusa.com/?xhtml=xhtml/eno/us/en/pressrelease/2009_03_30_manitowo

c_company_divest_enodis_ice_business.html&xsl=pressrelease.xsl>

5. Scotsman Industries. Warburg Pincus Completes Acquisition of Enodis Global Ice Machine 

Business from the Manitowoc Company: New Holding Company to be Named Scotsman 

Industries, Inc. (press release). May 18, 2009. Vernon Hills, IL.

<www.warburgpincus.com/PDF/Enodis%20PRESS%20RELEASE%205-15-

09%20final.pdf>

6. ALI Group, Ali Group announces the acquisition of Scotsman Industries, 

<http://www.aligroup.it/news/ali-group-announces-the-acquisition-of-scotsmanindustries/>

7. U.S. Small Business Administration. Small Business Size Standards Matched to North 

American Industry Classification System. 2006. (Last accessed June 25, 2007.) 

<www.sba.gov/gopher/Financial-Assistance/Size-Standards/sizetablestandards.txt> The 

June 25, 2007 material from this website is available in Docket EE-2006-STD-0126. For 

more information, contact Brenda Edwards-Jones at (202) 586-2945.

8. Standards Australia/Standards New Zealand. Performance of commercial ice makers and 

ice storage bins. Part 3: Minimum energy performance standard (MEPS) requirements. 

2004. AS/NZS 4865:3:2008.

3-51
9. U.S. Department of Energy–Office of Energy Efficiency and Renewable Energy. FEMP 

Covered Product Category: Air-Cooled Ice Machines. (Last accessed November 30, 2014.) 

<www1.eere.energy.gov/femp/technologies/eep_ice_makers.html> 

10. U.S. Department of Energy—Office of Energy Efficiency and Renewable Energy. FEMP, 

Covered Product Categories: Water-Cooled Ice Machines. The ice maker specifications 

are dated are dated August 2014. (Last accessed November 30, 2014.)

<http://energy.gov/eere/femp/covered-product-category-water-cooled-ice-machines>

11. Consortium for Energy Efficiency. Commercial Kitchens Initiative. (Last accessed 

November 30, 2014.) <http://www.cee1.org/content/cee-program-resources>

12. EnergySmart Grocer. EnergySmart Grocer Incentive Worksheet. 2014. (Last accessed 

November 30, 2014.) <http://www.energysmartgrocer.org/ca/documents/PG&E-ESGIncentiveWorksheet.pdf >

13. New York State Energy Research and Development Agency. Pre-Qualified Commercial 

Refrigeration Equipment. 2014. (Last accessed November 30, 2014.) 

<http://www.nyserda.ny.gov/Energy-Efficiency-and-Renewable-Programs/Commercialand-Industrial/CI-Programs/Existing-Facilities-Program/Pre-Qualified-Incentives.aspx >

14. North American Association of Food Equipment Manufacturers. 2010 Size and Shape of 

the Industry. 2010. Chicago, IL.

15. North American Association of Food Equipment Manufacturers. 2008 Size and Shape of 

the Industry. 2008. Chicago, IL.

16. North American Association of Food Equipment Manufacturers. 2006 Size and Shape of 

the Industry. 2006. Chicago, IL

17. Arthur D. Little, Inc. Energy Savings for Commercial Refrigeration. Final Report. June, 

1996. Submitted to the U.S. Department of Energy’s Energy Efficiency and Renewable 

Energy Building Technologies Program. Washington, D.C.

18. California Energy Commission. Update of Appliance Efficiency Regulations. 2004. 

Sacramento, CA.

19. Fernstrom, G. B. Analysis of Standards Options For Commercial Packaged Refrigerators, 

Freezers, Refrigerator-Freezers and Ice Makers: Codes and Standards Enhancement 

Initiative For PY2004: Title 20 Standards Development. 2004. Prepared by the American 

Council for an Energy-Efficient Economy for Pacific Gas & Electric Company, San 

Francisco, CA.

3-52
20. Navigant Consulting, Inc. Energy Savings Potential and R&D Opportunities for 

Commercial Refrigeration. Final Report. 2009. Submitted to the U.S. Department of 

Energy’s Energy Efficiency and Renewable Energy Building Technologies Program, 

Washington, D.C. 

<http://apps1.eere.energy.gov/buildings/publications/pdfs/corporate/commercial_refrig_rep

ort_10-09.pdf>

21. AHRI Ice Maker Shipment Data, submitted to DOE by Karim Amrane, May 2011.

22. Penney, K. Personal communication. PAX Scientific, Inc., San Rafael, CA. Email to 

Heather Lisle, Navigant Consulting, Inc. July 2011.

23. Biel, J. Personal communication. Scotsman Industries, Inc., Vernon Hills, IL. Telephone 

call with Heather Lisle, Navigant Consulting, Inc. April 2008.

24. Van Steenburgh, Jr., Leon R. Automatic Ice Cube Making Apparatus. U.S. Patent No. 

4,341,087, July 27, 1982.

25. Shedivy, Scott J. et al. Ice Machine with Assisted Harvest. U.S. Patent No. 6,681,580 B2, 

January 27, 2004.

3-53
CHAPTER 4. SCREENING ANALYSIS

TABLE OF CONTENTS

4.1 INTRODUCTION .............................................................................................................. 4-1

4.2 SCREENED-OUT TECHNOLGIES.................................................................................. 4-2

4.2.1 Compressor Part Load Operation............................................................................ 4-2

4.2.2 Enhanced Fin Surfaces............................................................................................ 4-2

4.2.3 Brazed Plate Condenser for Water-Cooled Condensers......................................... 4-2

4.2.4 Technology Options to Reduce Evaporator Thermal Cycling................................ 4-3

4.2.5 Technology Options that Reduce Harvest Meltage or Reduce Harvest Time ........ 4-3

4.2.6 Improved or Thicker Insulation .............................................................................. 4-3

4.2.7 Larger Diameter Suction Line (Remote Compressor Equipment) ......................... 4-3

4.2.8 Demand Response Controls.................................................................................... 4-3

4.3 REMAINING TECHNOLOGIES ...................................................................................... 4-4

LIST OF TABLES

Table 4.2.1 Screening Justification.............................................................................................. 4-4

4-i
CHAPTER 4. SCREENING ANALYSIS

4.1 INTRODUCTION

This chapter details the screening analysis that the U.S. Department of Energy (DOE)

conducted in support of the ongoing energy conservation standards rulemaking for automatic 

commercial ice-making (ACIM) equipment.

In the market and technology assessment—chapter 3 of the final rule technical support 

document (TSD)—DOE presents an initial list of technologies that can improve the energy 

efficiency of automatic commercial ice makers. The purpose of the screening analysis is to 

evaluate the technologies that improve equipment efficiency to determine which technologies to 

consider further and which to screen out. DOE consulted with a range of parties, including 

ACIM industry representatives, technical experts, and others, to develop a list of technologies for 

consideration. DOE evaluated the technologies pursuant to the criteria set out in the Energy 

Policy and Conservation Act (42 U.S.C. 6311–6317; EPCA), as amended.

EPCA establishes criteria for prescribing new or amended standards designed to achieve 

the maximum improvement in energy efficiency. Further, EPCA directs the Secretary of Energy 

to determine whether a standard is technologically feasible and economically justified. (42 

U.S.C. 6295(o)(2)(A)–(B) and 6313(d)(4))

Appendix A to subpart C of 10 CFR part 430, “Procedures, Interpretations and Policies 

for Consideration of New or Revised Energy Conservation Standards for Consumer Products”

(the Process Rule), sets forth procedures to guide DOE in its consideration and promulgation of 

new or revised equipment energy conservation standards. These procedures elaborate on the

statutory criteria provided in 42 U.S.C. 6295(o) and, in part, eliminate problematic technologies 

early in the process of prescribing or amending an energy conservation standard. In particular,

sections 4(b)(4) and 5(b) of the Process Rule guide DOE in determining whether to eliminate 

from consideration any technology that presents unacceptable problems with respect to the 

following criteria. To fulfill statutory criteria for prescribing energy standards for automatic 

commercial ice makers as established in 42 U.S.C. 6295(o) and 6313(d)(4), DOE applied the 

provisions in the Process Rule in its screening analysis for this rulemaking. 

Technological feasibility. Technologies incorporated in commercial equipment or in 

working prototypes will be considered technologically feasible.

Practicability to manufacture, install, and service. If mass production of a technology 

in commercial equipment and reliable installation and servicing of the technology could be 

achieved on the scale necessary to serve the relevant market at the time of the effective date of 

the standard, then that technology will be considered practicable to manufacture, install, and 

service.

Impacts on equipment utility or equipment availability. If a technology is determined 

to have a significant adverse impact on the utility of the equipment to significant subgroups of 

customers, or result in the unavailability of any covered equipment type with performance 

characteristics (including reliability), features, sizes, capacities, and volumes that are 

4-1
substantially the same as equipment generally available in the United States at the time, it will 

not be considered further.

Adverse impacts on health or safety. If it is determined that a technology will have 

significant adverse impacts on health or safety, it will not be considered further.

In sum, if DOE determines that a technology, or a combination of technologies, has 

unacceptable impacts on the policies stated in section 5(b) of the Process Rule, it will be 

eliminated from consideration. If a technology fails to meet one or more of the four criteria, it 

will be screened out. Section 4.2 documents the reasons for eliminating the technologies that 

were screened out.

4.2 SCREENED-OUT TECHNOLGIES

This section describes the technologies that DOE eliminated for failure to meet one of the 

following four criteria: (1) technological feasibility; (2) practicability to manufacture, install, and 

service; (3) impacts on equipment utility or equipment availability; and (4) adverse impacts on 

health or safety. The discussion also points out which technologies have not been analyzed in the 

engineering and downstream analyses for other reasons, including that they don’t save energy, 

their energy savings are not captured in the test procedure, or they are proprietary.

4.2.1 Compressor Part Load Operation

DOE has not changed its initial position on this technology option, first stated in the 

preliminary analysis. While there may be benefits associated with operation of an ice maker 

compressor at part load when ice demand is moderate or low, DOE is not aware of any 

commercialized product or prototype that has been constructed and tested to illustrate the 

benefits and/or reliability of this technology option. In addition, the DOE test procedure does not 

consider the benefits of part load operation. Hence, this technology option must be screened out 

based on the technical feasibility criterion.

4.2.2 Enhanced Fin Surfaces

DOE has not changed its initial position on this technology option, first stated in the 

preliminary analysis. DOE is not aware of any enhanced fin surfaces available for air-cooled 

condensers that would significantly improve heat transfer and efficiency in automatic 

commercial ice makers that are not already used extensively in the industry for air-cooled 

models. Hence, DOE is screening out this option because it is already in use and does not 

represent an option for improved efficiency.

4.2.3 Brazed Plate Condenser for Water-Cooled Condensers 

During the NOPR analyses, DOE concluded that use of brazed plate condensers does not 

represent a design option that would save energy in ice makers because most ice makers using 

water-cooled condensers have space for condenser surface area growth rather than requiring use 

of more expensive condenser technology such as brazed plate. DOE instead considered increases 

in condenser surface area based on testing conducted during the NOPR phase; these analyses are 

4-2
described in chapter 5 of the NOPR TSD. DOE has not altered this position during the final rule 

analysis.

4.2.4 Technology Options to Reduce Evaporator Thermal Cycling

During the NOPR analyses, DOE determined that the one technology used by

commercially available ice makers to reduce thermal mass is proprietary. The evaporators used 

by Hoshizaki America, Inc. contain proprietary elements that would make it difficult for others 

to replicate the design. Hence, DOE is screening out this option because of its proprietary status.

4.2.5 Technology Options that Reduce Harvest Meltage or Reduce Harvest Time

DOE has not changed its initial position on this technology option, first stated in the 

preliminary analysis. Manufacturers have implemented design changes to reduce harvest meltage 

and reduce harvest time in batch ice makers. DOE has been unable to identify additional design 

changes that are not already in use in the industry to further improve harvest cycle efficiency. 

Hence, this technology option is screened out because it does not save additional energy. 

4.2.6 Improved or Thicker Insulation

DOE has not changed its initial position on this technology option, first stated in the 

preliminary analysis. Insulation used in ice makers may reduce thermal loss, but, as indicated by 

stakeholders in comments (see chapter 2 of the preliminary analysis TSDa

), it is primarily used to 

prevent condensation of water on cold surfaces. A small amount of additional thermal loss could 

be prevented through the use of improved or thicker insulation. However, the impact of this 

efficiency improvement is small enough to be nearly immeasurable. Hence, this technology 

option has not been considered in the engineering and downstream analyses.

4.2.7 Larger Diameter Suction Line (Remote Compressor Equipment)

DOE has not changed its initial position on this technology option, first stated in the 

preliminary analysis. Increasing the suction line diameter could be considered to reduce suction 

line pressure drop for remote condenser equipment with remote compressors. However, the 

reduced suction vapor velocity associated with the approach could degrade oil return 

effectiveness. Remote ice maker line sets can be installed in the field so that suction line 

refrigerant runs up, down, or horizontally to the compressor; hence, they are conservatively sized 

to provide adequate oil return for a wide range of installation conditions. DOE has not 

considered increase in suction line size because of reliability concerns associated with potential 

oil hold-up and compressor failure associated with larger-diameter line sets.

4.2.8 Demand Response Controls

While there may be energy demand benefits associated with use of “smart technologies” 

in ice makers in that they reduce energy demand (e.g., shift the refrigeration system operation to 

a time of utility lower demand), DOE is not aware of any commercialized products or prototypes

a www.regulations.gov/#!documentDetail;D=EERE-2010-BT-STD-0037-0025

4-3

 
that also demonstrate improved energy efficiency in automatic commercial ice makers. Demand 

savings alone do not impact energy efficiency as measured in the test procedure, and hence, 

DOE cannot consider this technology in the analysis. 

Table 4.2.1 summarizes the technologies that were screened out for the final rule analysis 

or were not considered in the engineering analysis for other reasons. 

Table 4.2.1 Screening Justification

Design Option

EPCA Criteria for Screening Not Considered in the Analysis 

for Other Reasons

Technological 

Feasibility

Practicability to 

Manufacture, Install, 

and Service

Adverse Impacts on 

Product Utility

Adverse Impacts on 

Health and Safety

No 

Energy 

Savings or 

Savings not 

Measurable

Test 

Procedure 

Efficiency 

Metric 

Does 

Not 

Capture 

Savings

Sole-Source Proprietary 

Technology

Compressor Part Load Operation X X

Enhanced Fin Surfaces X

Brazed Plate Condenser X

Technology Options to Reduce 

Evaporator Thermal Cycling X

Technology Options Which Reduce 

Harvest Meltage or Reduce Harvest 

Time

X

Improved or Thicker Insulation X

Larger Diameter Suction Line X

Smart Controls X X

4.3 REMAINING TECHNOLOGIES

After eliminating those technologies that are proprietary, have no significant effect, or do 

not reduce energy use as measured by the test procedure, and screening out those technologies 

that do not meet the requirements of sections 4(a)(4) and 5(b) of the Process Rule, DOE 

considered the following technologies in the final rule analysis:

• improved compressor efficiency

• increased condenser surface area

• increased condenser air flow

• increased condenser water flow

• higher efficiency condenser fans and fan motors

• improved auger motor efficiency

• improved pump motor efficiency

• increased evaporator surface area

• reduced portable water flow

• use of drain water thermal exchange

4-4
CHAPTER 5. ENGINEERING ANALYSIS

TABLE OF CONTENTS

5.1 INTRODUCTION ........................................................................................................... 5-1

5.2 EQUIPMENT CLASSES ANALYZED.......................................................................... 5-1

5.3 METHODOLOGY OVERVIEW.................................................................................... 5-3

5.4 EFFICIENCY LEVELS................................................................................................... 5-4

5.4.1 Batch Ice Makers ................................................................................................. 5-4

5.4.1.1 Baseline: Existing DOE Standards .......................................................... 5-4

5.4.1.2 Maximum Available Efficiency Levels................................................... 5-4

5.4.1.3 Incremental Efficiency Levels................................................................. 5-5

5.4.1.4 Maximum Technology Level................................................................... 5-6

5.4.2 Continuous Ice Makers........................................................................................ 5-7

5.4.2.1 Energy Use Metric Incorporating Ice Hardness....................................... 5-7

5.4.2.2 Baseline Efficiency Levels ...................................................................... 5-8

5.4.2.3 Maximum Available Efficiency Levels................................................... 5-9

5.4.2.4 Incremental Efficiency Levels............................................................... 5-10

5.4.2.5 Maximum Technology Level................................................................. 5-10

5.5 DATA COLLECTION .................................................................................................. 5-11

5.5.1 Component Vendor Data ................................................................................... 5-11

5.5.2 Reverse Engineering .......................................................................................... 5-11

5.5.2.1 Selection of Products for Reverse Engineering and Inspections........... 5-12

5.5.2.2 Collection of Data to Support Energy Use Analysis ............................. 5-14

5.5.3 Manufacturer Interviews.................................................................................... 5-14

5.5.4 Energy Testing................................................................................................... 5-15

5.6 MANUFACTURING COST MODELING................................................................... 5-15

5.6.1 Generation of Bills of Materials ........................................................................ 5-15

5.6.2 Cost Structure of the Spreadsheet Models......................................................... 5-16

5.6.3 Cost Model and Definitions............................................................................... 5-16

5.6.3.1 Cost Definitions..................................................................................... 5-17

5.6.4 Cost Model Assumptions Overview .................................................................. 5-17

5.6.4.1 Fabrication Estimates............................................................................. 5-18

5.6.4.2 Factory Parameters Assumptions........................................................... 5-18

5.6.4.3 Material Cost Assumptions.................................................................... 5-19

5.6.5 Manufacturing Production Cost......................................................................... 5-19

5.6.6 Incremental Cost Estimates................................................................................ 5-20

5.6.6.1 G&A and Profit...................................................................................... 5-21

5.7 MANUFACTURER MARKUP .................................................................................... 5-21

5.8 ENERGY USE CALCULATIONS ............................................................................... 5-21

5.8.1 Energy Use Calculation Overview..................................................................... 5-21

5.8.2 Application of Design Options and Calibrations............................................... 5-22

5.9 DESIGN OPTIONS....................................................................................................... 5-23

5.9.1 Design Options That Were Not Considered for Further Analysis..................... 5-24

5.9.1.1 Increased Condenser Water Flow .......................................................... 5-24

5.9.2 Improved Compressor Efficiency...................................................................... 5-26

5-i
5.9.2.1 Compressor Cost Data ........................................................................... 5-26

5.9.3 Increased Air-Cooled Condenser Surface Area................................................. 5-28

5.9.4 Increased Water-Cooled Condenser Surface Area ............................................ 5-29

5.9.5 Evaporator Size Increase.................................................................................... 5-29

5.9.6 Design Options that Require Chassis Size Increases......................................... 5-30

5.9.7 Condenser Fan Motors....................................................................................... 5-30

5.9.8 Auger Motors..................................................................................................... 5-31

5.9.9 Pump Motors...................................................................................................... 5-32

5.9.10 Drain Water Heat Exchanger............................................................................. 5-32

5.9.11 Batch Water Fill and Reduced Potable Water Flow for Batch-Type Ice Makers.. 5-

33

5.10 ENGINEERING ANALYSIS RESULTS ..................................................................... 5-33

5.10.1 Overview............................................................................................................ 5-33

5.10.2 Design Option Analyses .................................................................................... 5-36

5.10.3 Design Option Calibrations................................................................................ 5-42

REFERENCES .......................................................................................................................... 5-59

LIST OF TABLES

Table 5.2.1 Batch Equipment Classes Analyzed in the Engineering Analysis............................ 5-2

Table 5.2.2 Continuous Equipment Classes Analyzed in the Engineering Analysis................... 5-2

Table 5.4.1 Existing DOE Standards for Batch Ice Makers........................................................ 5-4

Table 5.4.2 Batch Ice Maker Maximum Available Levels.......................................................... 5-5

Table 5.4.3 Incremental Efficiency Levels for Batch Ice Maker Equipment Classes................. 5-6

Table 5.4.4 Maximum Technology Levels for Batch Ice Maker Equipment Classes................. 5-7

Table 5.4.5 Baseline Efficiency Levels for Continuous Ice Maker Equipment Classes ............. 5-9

Table 5.4.6 Continuous Ice Maker Maximum Available Efficiency Levels............................... 5-9

Table 5.4.7 Incremental Efficiency Levels for Continuous Ice Maker Equipment Classes...... 5-10

Table 5.4.8 Maximum Technology Levels for Directly-Analyzed Continuous Ice Maker 

Equipment Classes............................................................................................. 5-10

Table 5.5.1 Component Vendors Contacted by DOE during Engineering Analysis................. 5-11

Table 5.5.2 Selected Units for Reverse Engineering and Energy Testing during Preliminary 

Analysis.............................................................................................................. 5-12

Table 5.5.3: Selected Units for Reverse Engineering and Energy Testing during NOPR Analysis

............................................................................................................................ 5-13

Table 5.5.4: Selected Units for Reverse Engineering and Energy Testing during Final Rule 

Analysis.............................................................................................................. 5-14

Table 5.6.1 Cost Model In-House Manufacturing Operation Assumptions.............................. 5-18

Table 5.6.2 ACIM Factory Parameter Assumptions.................................................................. 5-19

Table 5.8.1 IMH-A-Small-B Design Options............................................................................ 5-22

Table 5.9.1 Design Options by Equipment Class...................................................................... 5-24

Table 5.9.2 Condenser Water Flow Test Data for a Water-Cooled IMH Batch Unit................ 5-25

Table 5.9.3 Condenser Water Flow Test Data for a Water-Cooled SCU Continuous Unit ...... 5-25

Table 5.9.4 Condenser Water Flow Test Data for a Water-Cooled IMH Continuous Unit ...... 5-25

Table 5.9.5 AHRI 540 Medium Temperature Compressor Rating Test Conditions................. 5-26

5-ii
Table 5.9.6 Batch Ice Maker Evaluation of Harvest Cycle Dependence on Condensing 

Temperature ....................................................................................................... 5-28

Table 5.9.7 Description of Ice Making Head Size Increases in the Engineering Analysis ....... 5-30

Table 5.9.8 Motor Cost Information Use for Final Rule Engineering Analysis........................ 5-31

Table 5.9.9 Energy Consumption Tests with a Drain Water Heat Exchanger .......................... 5-32

Table 5.10.1 Batch Ice Maker Baseline Characteristics............................................................ 5-33

Table 5.10.2 Incremental Manufacturer Selling Price Results for Batch Ice makers................ 5-34

Table 5.10.3 Continuous Ice Maker Baseline Characteristics................................................... 5-35

Table 5.10.4 Incremental Manufacturer Selling Price Results for Continuous Ice Makers...... 5-35

Table 5.10.5 Directly Analyzed Equipment Classes Used to Develop Standards for Secondary 

Classes................................................................................................................ 5-35

Table 5.10.6 Manufacturing Cost and Energy Consumption Data for Air-Cooled IMH Batch Ice 

Machines with Capacities Less Than 450 lb/24 hours....................................... 5-36

Table 5.10.7 Manufacturing Cost and Energy Consumption Data for Air-Cooled IMH Batch Ice 

Machines with Capacities Greater Than 450 lb/24 hours (Smaller Sizes) ........ 5-36

Table 5.10.8 Manufacturing Cost and Energy Consumption Data for Air-Cooled IMH Batch Ice 

Machines with Capacities Greater Than 450 lb/24 hours (Larger Sizes).......... 5-37

Table 5.10.9 Manufacturing Cost and Energy Consumption Data for Water-Cooled IMH Batch 

Ice Machines with Capacities Less Than 500 lb/24 hours................................. 5-37

Table 5.10.10 Manufacturing Cost and Energy Consumption Data for Water-Cooled IMH Batch 

Ice Machines with Capacities between 500 and 1,436 lb/24 hours................... 5-37

Table 5.10.11 Manufacturing Cost and Energy Consumption Data for Water-Cooled IMH Batch 

Ice Machines with Capacities Greater Than 1,436 lb/24 hours (Smaller Sizes)5-38

Table 5.10.12: Manufacturing Cost and Energy Consumption Data for Water-Cooled IMH Batch 

Ice Machines with Capacities Greater Than 1,436 lb/24 hours (Larger Sizes) . 5-38

Table 5.10.13 Manufacturing Cost and Energy Consumption Data for Air-Cooled RCU Batch Ice

Machines with Capacities Greater Than 1000 lb/24 hours (Smaller Sizes) ...... 5-38

Table 5.10.14 Manufacturing Cost and Energy Consumption Data for Air-Cooled RCU Batch Ice 

Machines with Capacities Greater Than 1000 lb/24 hours (Larger Sizes)........ 5-39

Table 5.10.15 Manufacturing Cost and Energy Consumption Data for Air-Cooled SCU Batch Ice 

Machines with Capacities Less Than 175 lb/24 hours....................................... 5-39

Table 5.10.16 Manufacturing Cost and Energy Consumption Data for Air-Cooled SCU Batch Ice 

Machines with Capacities Greater Than 175 lb/24 hours.................................. 5-39

Table 5.10.17 Manufacturing Cost and Energy Consumption Data for Water-Cooled SCU Batch 

Ice Machines with Capacities Greater Than 200 lb/24 hours............................ 5-40

Table 5.10.18 Manufacturing Cost and Energy Consumption Data for Air-Cooled IMH 

Continuous Ice Machines with Capacities Less Than 700 lb/24 hours............. 5-40

Table 5.10.19: Manufacturing Cost and Energy Consumption Data for Air-Cooled IMH 

Continuous Ice Machines with Capacities Greater Than 700 lb/24 hours (Smaller 

Sizes).................................................................................................................. 5-40

Table 5.10.20 Manufacturing Cost and Energy Consumption Data for Air-Cooled SCU 

Continuous Ice Machines with Capacities Less Than 700 lb/24 hours............. 5-41

Table 5.10.21 Manufacturing Cost and Energy Consumption Data for Air-Cooled RCU 

Continuous Ice Machines with Capacities Less Than 850 lb/24 hours............. 5-41

Table 5.10.22 Manufacturing Cost and Energy Consumption Data for Air-Cooled IMH Batch Ice 

Machines with Capacities Less Than 450 lb/24 hours (22 inch Wide) ............. 5-41

5-iii
Table 5.10.23 Manufacturing Cost and Energy Consumption Data for Air-Cooled IMH Batch Ice 

Machines with Capacities Greater Than 450 lb/24 hours (Smaller Sizes, 22 inch 

Wide).................................................................................................................. 5-42

Table 5.10.24 Manufacturing Cost and Energy Consumption Data for Water-Cooled IMH Batch 

Ice Machines with Capacities Less Than 500 lb/24 hours (22 inch Wide) ....... 5-42

Table 5.10.25 Maximum Available Calibration Summary and Cost Differentials ................... 5-44

Table 5.10.26 Comparison of Analyzed and Max Available units for the IMH-W-Small-B 

Equipment Class................................................................................................ 5-45

Table 5.10.27 Comparison of Analyzed and Max Available units for the IMH-W-Small-B 

Equipment Class (22 inch wide)........................................................................ 5-45

Table 5.10.28 Comparison of Analyzed and Max Available units for the IMH-W-Med-B 

Equipment Class................................................................................................ 5-46

Table 5.10.29 Comparison of Analyzed and Max Available units for the IMH-W-Large-B-2 

Equipment Class................................................................................................ 5-46

Table 5.10.30 Comparison of Analyzed and Max Available units for the IMH-A-Small-B 

Equipment Class................................................................................................ 5-47

Table 5.10.31 Comparison of Analyzed and Max Available units for the IMH-A-Small-B 

Equipment Class (22 inch wide)........................................................................ 5-48

Table 5.10.32 Comparison of Analyzed and Max Available units for the IMH-A-Large-B-1 

Equipment Class................................................................................................ 5-49

Table 5.10.33 Comparison of Analyzed and Max Available units for the IMH-A-Large-B-1 

Equipment Class (22 inch wide)........................................................................ 5-50

Table 5.10.34 Comparison of Analyzed and Max Available units for the IMH-A-Large-B-2 

Equipment Class................................................................................................ 5-50

Table 5.10.35 Comparison of Analyzed and Max Available units for the RCU-NRC-Large-B-1 

Equipment Class................................................................................................ 5-51

Table 5.10.36 Comparison of Analyzed and Max Available units for the RCU-NRC-Large-B-2 

Equipment Class................................................................................................ 5-52

Table 5.10.37 Comparison of Analyzed and Max Available units for the SCU-A-Small-B 

Equipment Class................................................................................................ 5-53

Table 5.10.38 Comparison of Analyzed and Max Available units for the SCU-A-Large-B 

Equipment Class................................................................................................ 5-54

Table 5.10.39 Comparison of Analyzed and Max Available units for the SCU-W-Large-B 

Equipment Class................................................................................................ 5-55

Table 5.10.40 Comparison of Analyzed and Max Available units for the IMH-A-Small-C 

Equipment Class................................................................................................ 5-55

Table 5.10.41 Comparison of Analyzed and Max Available units for the IMH-A-Large-C 

Equipment Class................................................................................................ 5-56

Table 5.10.42 Comparison of Analyzed and Max Available units for the SCU-A-Small-C 

Equipment Class................................................................................................ 5-57

Table 5.10.43 Comparison of Analyzed and Max Available units for the RCU-A-Small-C 

Equipment Class................................................................................................ 5-58

LIST OF FIGURES

Figure 5.6.1 Full Production Costs............................................................................................ 5-20

5-iv
Figure 5.9.1 Final Rule Cost Curves for Medium-Temperature Compressors Used in Commercial 

Ice Makers.......................................................................................................... 5-27

5-v
CHAPTER 5. ENGINEERING ANALYSIS

5.1 INTRODUCTION

The engineering analysis establishes the relationship between manufacturer production 

cost (MPC) and energy consumption for the automatic commercial ice makers covered in this 

rulemaking. The “cost-efficiency” relationship serves as the basis for cost-benefit calculations in 

terms of individual customers, manufacturers, and the nation, which determines the most 

economically justified, technologically feasible standard level.

The inputs into the engineering analysis include baseline characteristics for each 

equipment class addressed in the market and technology assessment—chapter 3 of the final rule

technical support document (TSD)—the design options from the screening analysis (chapter 4), 

as well as cost and energy use data collected from manufacturers, component vendors, reverse

engineering, and energy testing. The output of the engineering analysis is the cost-efficiency 

relationship for each equipment class, which was used in the life-cycle cost (LCC) and payback 

period analyses (chapter 8 of the final rule TSD) as well as the national impacts analyses (chapter 

10 of the final rule TSD). This chapter covers the equipment classes that the U.S. Department of 

Energy (DOE) analyzed; the methodology used by DOE to develop manufacturing costs and 

energy consumption estimates for the final rule analysis phase of the rulemaking; and the 

engineering analysis results. 

5.2 EQUIPMENT CLASSES ANALYZED

The final rule engineering analysis is based on the study of 25 equipment classes derived from 

the existing DOE batch commercial ice maker standards, effective as of January 1, 2010 (42 

U.S.C. 6313(d)(1)), and investigation of the existing continuous ice maker market. DOE 

analyzed 14 of these equipment classes directly through representative units with given 

characteristics, including type of ice making process, configuration, condenser cooling, and 

harvest rate. Each analysis involved energy testing and reverse engineering, as well as analysis of 

energy use and manufacturing cost to develop a cost-efficiency relationship for potential design 

options to reduce energy use. DOE selected the representative units to reflect the general design, 

and performance of ice makers within their equipment classes. Table 5.2.1 and Table 5.2.2 list 

the batch and continuous ice maker equipment classes that DOE directly analyzed in the final 

rule engineering analysis, respectively.

a The equipment classes DOE selected for direct analysis 

represent close to 100 percent of all shipments of automatic commercial ice makers. 

a See chapter 3 of the final rule TSD for description of batch and continuous ice maker categories.

5-1

 
Table 5.2.1 Batch Equipment Classes Analyzed in the Engineering Analysis

Equipment Type

Condenser 

Cooling

Type

Harvest Capacity Rate

lb/24 hours Abbreviation*

Reverse Engineering 

Unit, Directly 

Analyzed Equipment 

Class

Ice-Making Head

Water

Small (<500) IMH-W-Small-B 

Medium (≥500 and <1,436) IMH-W-Med-B 

Large (≥1,436) IMH-W-Large-B 

Air Small (<450) IMH-A-Small-B 

Large (≥450) IMH-A-Large-B 

Remote Condensing 

(but not remote 

compressor)

Air

Small (<1,000) RCU-NRC-Small-B

Large (≥1,000) RCU-NRC-Large-B 

Remote Condensing 

and Remote 

Compressor

Air

Small (<1,000) RCU-RC-Small-B

Large (≥1,000) RCU-RC-Large-B 

Self-Contained Unit

Water Small (<200) SCU-W-Small-B

Large (≥200) SCU-W-Large-B 

Air Small (<175) SCU-A-Small-B 

Large (≥175) SCU-A-Large-B 

*Abbreviation notation: system type (batch (B) or continuous (C)). Equipment type (ice-making head (IMH), remote condenser unit 

(RCU), self-contained unit (SCU)). Condenser cooling (air (A) or water (W)) or compressor location for RCUs (remote compressor 

(RC), non-remote compressor (NRC)). 

Table 5.2.2 Continuous Equipment Classes Analyzed in the Engineering Analysis

Equipment Type

Condenser 

Cooling

Type

Harvest Capacity Rate

lb/24 hours

Equipment Class 

Abbreviation

Reverse Engineering 

Unit, Directly Analyzed 

Equipment Class

Ice-Making Head

Water Small (<900) IMH-W-Small-C

Large (≥900) IMH-W-Large-C

Air Small (<700) IMH-A-Small-C 

Large (≥700) IMH-A-Large-C 

Remote Condensing 

(but not remote 

compressor)

Air

Small (<850) RCU-NRC-Small-C 

Large (≥850) RCU-NRC-Large-C

Remote Condensing 

(with remote 

compressor)

Air

Small ( <850) RCU-RC-Small-C

Large (≥850) RCU-RC-Large-C

Self-Contained Unit

Water Small (<900) SCU-W-Small-C

Large (≥900) SCU-W-Large-C *

Air Small (<700) SCU-A-Small-C 

Large (≥700) SCU-A-Large-C *

*DOE was not able to identify any existing products of this equipment class in ice maker databases. Hence, this equipment class was 

not analyzed, directly or by extrapolation.

In order to perform the analysis as efficiently as possible, DOE did not directly analyze 

all covered equipment classes. DOE did not conduct full separate analyses for remote condensing 

unit ice makers with and without remote compressors. DOE considered analyses for the remote 

condensing without remote compressor equipment to be representative of equipment classes both 

with and without remote compressors. This simplification is discussed in greater detail in section

5-2
5.4.1.4. For those classes not directly analyzed (the secondary equipment classes), DOE 

represented the cost/efficiency relationship using the results for directly analyzed equipment 

classes with similar design characteristics. The representation by directly-analyzed classes of the 

secondary classes is discussed in section 5.10.1

In some cases, DOE conducted multiple analyses within a given equipment class in order 

to obtain a more accurate representation of units within that class. For the equipment classes 

RCU-NRC/RC-Large-B, IMH-W-Large-B, and IMH-A-Large-B, DOE considered two different 

sizes of equipment to more accurately represent the large range of harvest rates within these 

classes. Additionally, DOE developed additional cost-efficiency curves to represent space

constrained, 22 inch wide units within the equipment classes IMH-A-Small-B, IMH-W-Small-B, 

and IMH-A-Large-B (for that last of these classes at a 800 lb/24 hours harvest capacity).

5.3 METHODOLOGY OVERVIEW

This section describes the analytical methodology DOE used in the engineering analysis. 

In this rulemaking, DOE has adopted a combined efficiency level/design option/reverse 

engineering approach to developing cost-efficiency curves. DOE established efficiency levels 

defined as percent energy use lower than that of baseline efficiency products. DOE’s analysis is 

based on the efficiency improvements associated with groups of design options. Concurrently, 

DOE developed manufacturing cost models based heavily on reverse engineering of products to 

develop a baseline MPC and to support calculation of the incremental costs associated with 

improvement of efficiency.

The engineering analysis process began with data collection and ended with the 

incremental cost curve results. To develop these cost-efficiency curves, DOE collected 

information from various sources on the manufacturing cost and energy use reduction 

characteristics of each of the design options. DOE reviewed product literature, conducted reverse

engineering of current products, and interviewed component vendors of compressors and fan 

motors. DOE also conducted interviews with manufacturers throughout the rulemaking, 

including during the final rule analyses to follow up on the information collected during the 

preliminary and NOPR analyses. In particular, DOE actively sought additional feedback in terms 

of component costs to calibrate the outputs of the manufacturing cost models.

Cost information from the vendor interviews and engineering questionnaires during the 

preliminary and NOPR phases provided initial input to the manufacturing cost model. DOE 

determined incremental costs associated with specific design options using vendor information 

and the cost model. DOE made adjustments to the cost assumptions based on available sources, 

including manufacturer interviews conducted throughout the rulemaking. DOE calculated energy 

use characteristics for different design configurations representing the directly-analyzed 

equipment classes. Input for the energy use analysis included data from experimental testing; 

publicly available information such as ratings databases, and interviews with manufacturer and 

vendors. The incremental cost estimates and the energy modeling results together constitute the 

energy efficiency curves presented in this chapter.

5-3
5.4 EFFICIENCY LEVELS

5.4.1 Batch Ice Makers

This section discusses the baseline, incremental, and maximum technology efficiency 

levels that DOE used in its final rule analysis for automatic commercial ice makers of the batch 

equipment classes.

5.4.1.1 Baseline: Existing DOE Standards

The Energy Policy and Conservation Act (EPCA), as amended by the Energy Policy Act 

of 2005 (EPACT 2005), prescribed the following standards for batch ice makers, shown in 

Table 5.4.1, effective January 1, 2010. (42 U.S.C. 6313(d)(1)) These efficiency levels represent 

the baseline efficiency levels for batch ice makers. DOE also adopted these baseline efficiency 

levels for ice makers with ice harvest capacities of up to 4,000 pounds of ice per 24 hours,b

except that a more complicated approach may be adopted for large air-cooled ice-making head 

ice makers with harvest capacity rate larger than 1,600 lb/24 hours.

Table 5.4.1 Existing DOE Standards for Batch Ice Makers

Equipment Type Type of 

Cooling

Harvest Capacity Rate

lb/24 hours

Maximum Energy 

Use 

kWh/100 lb ice*

Maximum Condenser 

Water Use*, **

gal/100 lb ice

Ice-Making Head

Water

Small (<500) 7.80–0.0055H 200–0.022H

Medium (≥500 and <1,436) 5.58–0.0011H 200–0.022H

Large (≥1,436) 4.0 N/A

Air Small (<450) 10.26–0.0086H N/A

Large (≥450) 6.89–0.0011H N/A

Remote Condensing 

(but not Remote 

Compressor)

Air

Small (<1,000) 8.85–0.0038H N/A

Large (≥1,000) 5.1 N/A

Remote Condensing 

and Remote 

Compressor

Air

Small (<934) 8.85–0.0038H N/A

Large (≥934) 5.3 N/A

Self-Contained Unit

Water Small (<200) 11. 40–0.019H 191–0.0315H

Large (≥200) 7.6 191–0.0315H

Air Small (<175) 18.0–0.0469H N/A

Large (≥175) 9.8 N/A

* H = Harvest rate in pounds per 24 hours.

** Water use is for the condenser only and does not include potable water used to make ice.

5.4.1.2 Maximum Available Efficiency Levels

Maximum available efficiency levels for the analyzed equipment classes are tabulated in

Table 5.4.2. DOE removed from consideration as maximum available those batch ice makers that 

use proprietary technology or those that are known to make half-cube ice. The energy use 

focused on full-cube ice makers because half-cube ice makers may have an efficiency advantage. 

b For brevity, pounds of ice per 24 hours will be referred to herein as lb ice/24 hours.

5-4

 
This information is based on a survey of product databases and manufacturer websites (also see 

data in chapter 3 of the final rule TSD). The maximum available efficiency levels are represented 

by percentage energy use less than the energy use of baseline-efficiency equipment, which has 

been selected as equal to the current DOE energy standard for batch ice makers. DOE used the 

maximum available efficiency levels to guide selection of incremental efficiency levels.

Table 5.4.2 Batch Ice Maker Maximum Available Levels 

Equipment 

Type

Type of 

Cooling

Harvest Capacity Rate

lb/24 hours

Max Available 

Efficiency 

Level*

Harvest 

Capacity

lb/24 hours

Brand & Model Number

Ice-Making 

Head

Water

Small (<500) 19.2% 360 Manitowoc IY-0455W

Small (<500) 

(22 inch wide) 16.9% 280 Scotsman C0322MW

Medium (≥500 and 

<1,436) 14.3% 832 Scotsman C0830MW

Large (≥1,436) 5.0% 1628 Cornelius CCM1848WF2

Air

Small (<450) 19.3% 300 Manitowoc KD-0350A

Small (<450)

(22 inch wide) 16.6% 370 Ice-O-Matic ICE0520FA

Large (≥450 and < 875) 16.1% 844 Scotsman C1030MA-32B

Large (≥450 and < 875)

(22 inch wide) 5.5% 590 Scotsman C0722MA-32#

Extended (≥ 875) 6.0% 1527 Scotsman C1848MA

Remote 

Condensing 

(but not 

Remote 

Compressor)

Air

Small (<1,000) 25.8% 520 Ice-O-Matic ICE0606FR

Large (≥1,000) 15.7% 1645 Scotsman C1848MR / 

ERC611-32A

Large (≥1,000) 14.9% 2256 Scotsman C2648MR / 

PRC241-32A

Remote 

Condensing 

and Remote 

Compressor

Air

Small (<934) 20.4% 696 Manitowoc ID0976C-161

Large (≥934) 15.7% 1,132 Manitowoc ID1472C-161

SelfContained 

Unit

Water Small (<200) 26.2% 141 Manitowoc UD0240W-261

Large (≥200) 27.6% 285 Scotsman CU3030MW, SW-1#

Air Small (<175) 30.7% 130 Ice-O-Matic ICEU150FA

Large (≥175) 26.4% 247 ITV Spika NG285-A1

* Percent energy use lower than the baseline efficiency level (i.e., the current DOE standard)

5.4.1.3 Incremental Efficiency Levels

DOE established a series of incremental efficiency levels for each batch ice maker 

equipment class and developed corresponding incremental cost data that quantifies a costefficiency relationship. DOE established the highest incremental efficiency levels based on a 

review of the maximum efficiency levels of available products, as discussed in section 5.4.1.2. 

Table 5.4.3 shows the selected incremental efficiency levels.

5-5
Table 5.4.3 Incremental Efficiency Levels for Batch Ice Maker Equipment Classes 

Equipment Type*

Representative Harvest 

Capacity Rate

lb/24 hours

EL2**

EL3

EL 3A

***

EL4

EL 4A

***

EL5 EL6 EL7

IMH-W-Small-B 300 10% 15% 20%

21.5% 24%

IMH-W-Med-B 850 10% 15% 18%

IMH-W-Large-B-1 1,500 8%

IMH-W-Large-B-2 2,600 7%

IMH-A-Small-B 300 10% 15% 

18% 20% 25% 26%

IMH-A-Large-B-1 800 10% 15% 

16% 20%

IMH-A-Large-B-2 1,500 10% 12%

RCU-NRC/RCSmall-B Not Directly Analyzed

RCU-NRC/RCLarge-B

1,500 10% 15% 17%

2,400 10% 14%

SCU-W-Small-B Not Directly Analyzed

SCU-W-Large-B 300 10% 15% 20% 25% 30%

SCU-A-Small-B 110 10% 15% 20% 25% 30% 33%

SCU-A-Large-B 200 10% 15% 20% 25% 28%

* See Table 5.2.1 for a description of these abbreviations.

** EL = efficiency level; EL1 is the baseline efficiency level, while EL2 through EL7 represent increased 

efficiency levels.

***DOE considered intermediate efficiency levels 3A and 4A for some equipment classes.

For those classes not directly analyzed (the secondary equipment classes), DOE 

represented the cost/efficiency relationship using the results for directly analyzed equipment 

classes with similar design characteristics. The representation by directly-analyzed classes of the 

secondary classes is discussed in section 5.10.1

5.4.1.4 Maximum Technology Level

DOE defines maximum technology levels for each equipment class to represent the 

maximum possible efficiency if all design options are incorporated. The maximum technology 

level is not necessarily the same as the maximum available level, which is the highest efficiency 

unit currently available on the market. In some cases, the maximum technology level is not 

commercially available because it is not economically feasible. In other cases, the maximum 

technology level does not incorporate all design options used in maximum available efficiency 

products because sole-source proprietary technologies are not considered in the analysis.

DOE determined maximum technology levels based on its energy use analysis. Namely, 

the maximum technology levels were calculated based on incorporating all design options 

applicable for each equipment class. The maximum technology efficiency levels for the directly 

analyzed batch ice maker equipment classes are presented in Table 5.4.4. The design options 

considered in the analysis are listed for each equipment class in section 5.10.2. For some 

equipment classes, the table shows different maximum technology levels for the multiple 

representative capacities included in the analysis for these classes.

5-6
Table 5.4.4 does not show maximum technology levels separately for remote condenser 

units with and without remote compressors. During the preliminary analysis, DOE considered 

whether the efficiency improvements associated with the design options used in the engineering 

analysis would be significantly different for remote compressor ice makers as compared with 

non-remote compressor models. DOE concluded that there would be little differences in the 

magnitudes or costs of these improvements as the design options are applied to these two types 

of ice makers. In the final rule analysis, DOE did not consider remote compressor and nonremote compressor units separately. This is discussed in more detail in chapter 5 of the 

preliminary analysis TSD.

Table 5.4.4 Maximum Technology Levels for Batch Ice Maker Equipment Classes

Equipment Class Energy Use Lower than Baseline

IMH-W-Small-B 23.9%

IMH-W-Small-B

(22 inch wide) 21.5%

IMH-W-Med-B 18.1%

IMH-W-Large-B 8.3% (at 1,500 lb/24 hours)

7.4% (at 2,600 lb/24 hours)

IMH-A-Small-B 25.5%

IMH-A-Small-B

(22 inch wide) 18.1%

IMH-A-Large-B-1

IMH-A-Large-B-2

23.4% (at 800 lb/24 hours)

11.8% (at 1,500 lb/24 hours)

IMH-A-Large-B-1

(22 inch wide) 15.8%

RCU-Small-B Not directly analyzed – similar to RCU-Large-B

RCU-Large-B 17.3% (at 1,500 lb/24 hours)

13.9% (at 2,400 lb/24 hours)

SCU-W-Small-B Not directly analyzed - similar to SCU-W-Large-B

SCU-W-Large-B 29.8%

SCU-A-Small-B 32.7%

SCU-A-Large-B 29.1%

5.4.2 Continuous Ice Makers

5.4.2.1 Energy Use Metric Incorporating Ice Hardness

Continuous-type ice makers typically produce ice that is not completely frozen, leaving 

some liquid water in the total mass of ice produced. The ice hardness (for a 32 °F ice product, the 

percentage of frozen ice) is directly related to the measured energy consumption of these 

machines. To account for this impact on energy use, the test procedure final rule for automatic 

commercial ice makers requires that the measured energy use of continuous machines be 

adjusted based on the ice hardness. 77 FR 1591 (Jan. 11, 2012).

The final test procedure calls for use of a calorimeter to determine ice hardness, and 

calculation of an ice quality adjustment factor based on the calorimeter test results as follows:

5-7
Ice Hardness Adjustment Factor = � 144 Btu

lb � + 38 Btulb �

�144 Btu

lb � ×

IIIIII HHHHHHHHHH HHHH FF FFFF

100 � + 38 Btu

lb �

�

Eq. 5.1

The measured energy consumption per 100 lb of ice and the measured condenser water 

consumption, as determined using American Society of Heating, Refrigerating and AirConditioning Engineers (ASHRAE) Standard 29-2009, would be multiplied by the ice hardness

adjustment factor to yield the adjusted energy and condenser water consumption values, 

respectively. DOE’s consideration of continuous ice maker efficiency levels and its rulemaking 

analysis in general are based on use of the adjusted energy and condenser use values.

5.4.2.2 Baseline Efficiency Levels

There currently are no DOE energy standards for continuous ice makers. For the 

preliminary analysis, DOE chose to develop efficiency levels by fitting baseline efficiency level 

curves to collected existing ice maker data. DOE has updated the baseline efficiency levels in 

response to the availability of new data during the course of the rulemaking, first for the NOPR, 

and also for the final rule.

DOE developed baseline efficiency levels using energy use data available from several 

sources, as discussed in chapter 3 of the final rule TSD. DOE chose baseline efficiency levels 

that would be met by nearly all ice makers represented in the databases. DOE primarily 

considered those units for which ice hardness data was available. For units that did not have 

reported ice hardness data, DOE used assumptions for the ice hardness factor of 70 and 85 for 

flake and nugget ice makers, respectively. The baseline efficiency levels are tabulated in Table 

5.4.5, and they are plotted with the ice maker energy use data in chapter 3 of the final rule TSD.

5-8
Table 5.4.5 Baseline Efficiency Levels for Continuous Ice Maker Equipment Classes

Equipment Type Type of Cooling Harvest Capacity Rate 

lb/24 hours

Energy Use

kWh/100 lb ice*

Ice-Making Head

Water Small (<900) 8.1–0.00333H

Large (≥900) 5.1

Air Small (<700) 11.0–0.00629H

Large (≥700) 6.6

Remote Condensing (but 

not Remote Compressor) Air Small (<850) 10.0–0.00459H

Large (≥850) 6.1

Remote Condensing and 

Remote Compressor Air

Small (<850) 10.2–0.00459H

Large (≥850) 6.3

Self-Contained Unit

Water Small (<900) 9.5–0.00378H

Large (≥900) 6.1

Air

Small (<700) 16.3–0.03H

Large (≥200 and < 700) 11.84 – 0.0078H

Extended (≥ 700) 6.38

* H = Harvest capacity in lb/24 hours.

5.4.2.3 Maximum Available Efficiency Levels

Maximum available efficiency levels for the analyzed equipment classes are tabulated in

Table 5.4.6. This information is based on a survey of product databases and manufacturer 

websites (also see data in chapter 3 of the final rule TSD). These levels are based on AHRI 

product directory data that includes ice hardness data. The maximum available efficiency levels 

are represented by percentage energy use less than that of baseline-efficiency equipment. DOE 

used the maximum available efficiency levels to select appropriate ranges of incremental 

efficiency levels.

Table 5.4.6 Continuous Ice Maker Maximum Available Efficiency Levels 

Equipment 

Type

Type of 

Cooling

Harvest 

Capacity Range

Max Available

Efficiency level

Harvest 

Capacity

lb/24 hours

Brand & Model Number

Ice-Making 

Head

Water Small 17% 532 Hoshizaki RF0650W-161

Large 12% 1,122 Manitowoc RFS1200W-261

Air Small 28% 531 Hoshizaki F-801MAH

Large 36% 845 Ice-O-Matic MFI1256

Remote 

Condensing 

(but not remote 

compressor)

Air

Small 18% 835 Scotsman N0922R-32#

Large 19% 1,050 Scotsman F1222R-32#

Remote 

Condensing 

and Remote 

Compressor

Air Small 8% 713 Manitowoc RN1078C-161

Air Large 14% 962 Manitowoc RN1278C-161

Self-Contained 

Unit

Water Small 19% 310 Scotsman AFE400W-1#

Large No products in this equipment class*

Air Small 29% 325 Follet 12CI400A

Large No products in this equipment class*

* DOE’s market analysis showed that there were no products in this equipment class.

5-9
5.4.2.4 Incremental Efficiency Levels

DOE established a series of incremental efficiency levels for each analyzed continuous 

ice maker equipment class and developed corresponding incremental cost data that quantifies a 

cost-efficiency relationship. DOE established the highest incremental efficiency levels based on 

a review of the maximum efficiency levels of available equipment, as discussed in section 

5.4.2.3. Table 5.4.7 shows the selected incremental efficiency levels. The efficiency levels are 

defined by the percent energy use less than the trial baseline energy use.

Table 5.4.7 Incremental Efficiency Levels for Continuous Ice Maker Equipment Classes 

Equipment Type*

Harvest 

Capacity

lb/24 hours

EL2** EL3 EL4 EL5 EL6

IMH-W-S-C 800 Not Directly Analyzed

IMH-W-L-C 1000 Not Directly Analyzed

1800 Not Directly Analyzed

IMH-A-S-C 310 10% 15% 20% 25% 26%

IMH-A-L-C 820 10% 15% 20% 23%

1800 Not Directly Analyzed

RCU-S-C 800 10% 15% 20% 25% 27%

RCU-L-C 1500 Not Directly Analyzed

SCU-W-S-C 300 Not Directly Analyzed

SCU-W-L-C NA No products in this equipment class‡

SCU-A-S-C 220 7% 15% 20% 25% 27%

SCU-A-L-C NA No products in this equipment class‡

* See Table 5.2.2 for a description of these abbreviations. 

** EL = efficiency level; EL1 is the base line efficiency level while EL2 through EL5 represent increased 

efficiency levels.

‡ DOE’s market analysis showed that there were no products in this equipment class.

5.4.2.5 Maximum Technology Level

The maximum technology efficiency levels for the directly-analyzed continuous ice 

maker equipment classes are presented in Table 5.4.8. The design options considered in the 

analysis are listed for each equipment class in section 5.10.2. For two equipment classes, SCUW-Large-C and SCU-A-Large-C, DOE was not able to find ice makers in publicly available

databases and/or manufacturer websites meeting the harvest capacity criteria for these classes. 

Table 5.4.8 Maximum Technology Levels for Directly-Analyzed Continuous Ice Maker 

Equipment Classes

Equipment Class Energy Use Lower than Baseline

IMH-A-Small-C 25.7%

IMH-A-Large-C 23.3%

RCU-NRC-Small-C 26.6%

SCU-A-Small-C 26.6%

5-10
5.5 DATA COLLECTION

5.5.1 Component Vendor Data

DOE contacted major suppliers of key commercial ice maker components to obtain 

performance and cost data to support its design option analysis. This effort consisted of phone 

interviews and email correspondence. Table 5.5.1 lists the vendors contacted. DOE also obtained 

from the compressor vendors or their websites’ complete performance data for compressors used 

in the energy analyses, including analyses for baseline and improved-efficiency configurations.

Table 5.5.1 Component Vendors Contacted by DOE during Engineering Analysis

Component Type Vendors

Compressors

Bristol

Copeland

Danfoss

Embraco

Tecumseh

Condenser Fan Motors

A.O. Smith

Bohn

EBM Papst

Emerson Climate Technologies 

Fasco 

Marathon Electric

Morrill Motors

Regal Beloit Electric Motors

Zhongshan Broad Ocean Motors

Auger Motors

Brother International

Emerson Industrial Automation

Bison Gear Motors

Pump Motors

Fasco

Hartell 

Morrill Motors

Sanso Electric

Heat Exchangers

Heatcraft

Modine

AlCoil

5.5.2 Reverse Engineering

DOE purchased a number of automatic commercial ice makers, chosen to represent the 

designs of various equipment classes, in order to examine design and fabrication details. This 

reverse engineering included detailed measurement of dimensions, system and component-level 

power measurements, measurement of air flows for products with forced convection heat 

exchangers, and physical teardowns. The data collected in the reverse engineering process were 

used in both manufacturing cost modeling and energy use modeling. This section describes the 

selection of products for reverse engineering as well as some of the measurements made to 

support subsequent modeling. Section 5.6 more thoroughly describes the physical teardown 

process used to support manufacturing cost modeling. 

5-11
5.5.2.1 Selection of Products for Reverse Engineering and Inspections

Table 5.5.2 describes the 19 products selected for reverse engineering during the 

preliminary analysis and identifies the products for which DOE conducted energy tests (see 

section 5.5.4 for more on energy testing). DOE aimed to select a range of models that would 

cover a broad spectrum of equipment classes and capacities based on the California Energy 

Commission (CEC), ENERGY STAR, and AHRI databases, as well as manufacturer websites.

Table 5.5.2 Selected Units for Reverse Engineering and Energy Testing during Preliminary 

Analysis

Type Equipment Class

Harvest 

Capacity

lb/24 hours

Energy 

Use

kWh/

100 lb

Potable 

Water Use 

gal/100 lb

Energy 

Test

Reverse 

Engineering

Batch

IMH-A-Small-B 270 7.70 20.0  

IMH-A-Small-B 324 5.80 22.6  

IMH-A-Large-B 780 5.70 20.5 

IMH-A-Large-B 844 5.00 18.4 

IMH-A-Large-B 1,460 4.95 20.0 

IMH-A-Large-B 1,560 4.10 18.8  *

IMH-W-Large-B 851 4.40 19.7 

IMH-W-Large-B 2,820 3.60 19.7  

RCU-A-Large-B 1,510 4.59 20.0 

RCU-A-Large-B 1,694 3.80 18.1  *

RCU-A-Large-B 2,350 4.64 19.7 

SCU-A-Small-B 121 8.40 17.8  

SCU-A-Small-B 112 11.80 34.0 

SCU-W-Large-B 285 5.50 18.0  

Continuous

IMH-A-Small-C 310 6.31 12.0 

IMH-A-Small-C 822 5.51 13.6 

IMH-A-Small-C 845 3.40 12.0  

RCU-A-Large-C 1780 3.40 12.0  *

SCU-A-Large-C 280 6.00 19.0  

During the NOPR analyses, DOE performed energy testing and reverse engineering of 

additional ice maker products. DOE aimed to select a range of models that would complement 

the existing set of models and provide a set of baseline and high-efficiency models in several key 

equipment classes. For these models, reverse engineering consisted of product inspections that 

captured the vital components and characteristics of each product. DOE also performed energy 

modeling of three products to further the cost-efficiency analysis. Table 5.5.3 describes the 20 

additional products analyzed during the NOPR phase.

5-12
Table 5.5.3: Selected Units for Reverse Engineering and Energy Testing during NOPR 

Analysis

Type Equipment Class

Harvest 

Capacity

lb/24 

hours

Energy 

Use

kWh/

100 lb

Potable 

Water Use 

gal/100 lb

Energy 

Test

Reverse 

Engineering

Batch

IMH-A-Small-B 335 6.0 20.0  

IMH-A-Large-B 528 6.0 19.1  

IMH-W-Small-B 360 4.7 20.0  

IMH-W-Small-B 270 6.19 23.9  

IMH-W-Small-B 275 6.1 39.0  

IMH-W-Medium-B 650 4.1 18.6  

IMH-W-Medium-B 832 4.0 18.9  

RCU-NRC-Small-B 780 4.7 18.4  

RCU-NRC-Small-B 843 5.6 22.8  

RCU-NRC-Large-B 2256 4.34 17.0  

SCU-A-Small-B 143 8.48 32.7  

SCU-A-Large-B 204 9.5 32.4  

SCU-A-Large-B 228 8.6 32.9  

Continuous

IMH-A-Large-C- 921 6.6* 12.0  

IMH-A-Large-C 1100 5.5* 12.0  

IMH-W-Small-C 560 5.9* 12.0  

RCU-NRC-Small-C 610 6.9* 12.0  

RCU-NRC-Small-C 835 5.0* 12.0  

SCU-A-Small-C 250 9.1* 12.0  

SCU-W-Small-C 455 5.0* 12.0  

* Adjusted energy use calculated using the reported ice quality of the unit

During the final rule analyses, DOE performed energy testing and reverse engineering of 

additional ice maker products. DOE selected models to obtain information about both baseline 

and high efficiency units within directly analyzed equipment classes for which DOE previously 

did not have this information. Reverse engineering of these models involved inspections of the 

key components and characteristics of each product. Table 5.5.4 describes the 13 additional 

products analyzed during the final rule phase.

5-13
Table 5.5.4: Selected Units for Reverse Engineering and Energy Testing during Final Rule

Analysis

Type Equipment Class

Harvest 

Capacity

lb/24 

hours

Energy 

Use

kWh/

100 lb

Potable 

Water Use 

gal/100 lb

Energy 

Test

Reverse 

Engineering

Batch

IMH-A-Large-B 714 6.1 20  

IMH-A-Large-B 590 5.9 18  

IMH-A-Large-B 1242 4.6 18.5  

IMH-A-Large-B 760 5.2 12  

IMH-A-Small-B 300 6.2 22  

IMH-A-Small-B 380 6.7 18.5  

IMH-A-Small-B 370 5.9 23.1  

RCU-NRC-Large-B 1645 4.3 17.9  

SCU-A-Small-B 110 11.5 18  

SCU-A-Small-B 130 8.25 25  

SCU-W-Large-B 296 6.5 35.6  

Continuous IMH-A-Small-C 531 5.5 12  

RCU-NRC-Small-C 713 6.4 12  

5.5.2.2 Collection of Data to Support Energy Use Analysis

DOE examined each unit prior to teardown to record details that are key parameters in 

subsequent energy use analysis. The key measurements are described in this section. The rated 

capacity and energy use for each product were based on the AHRI database or on product 

literature. DOE contracted with Intertek Testing Services, Inc. (Intertek) and United Laboratories

(UL) for testing, which was conducted according to the DOE test procedure. 

DOE measured component-level power input for fans, water pumps, and other 

components (e.g., air pumps used for harvest assist, solenoid valves, etc.). For some of the 

continuous ice makers, DOE separately measured augur motor power input while the ice maker 

was making ice. 

DOE made air flow measurements for air-cooled condensers using a hot wire 

anemometer. The location of these measurements varied, depending on the heat exchanger type 

and configuration. The determination of air flows based on these measurements is not very 

reliable, so this measurement was used as an indication of air flow trends more than exact 

indication of air flow for the various products.

Details of the cabinet size, heat exchanger dimensions, insulation thickness, etc. were 

based on direct physical measurements made during the teardown process. DOE noted condenser 

details, such as type, configuration, numbers of tubes and fins, and dimensions. The details of 

evaporators and suction line heat exchangers were similarly determined during teardown. DOE 

recorded component manufacturer and model data for key components such as compressors, 

fans, and controls.

5.5.3 Manufacturer Interviews

The manufacturer interviews conducted during the preliminary analysis are discussed in 

chapter 5 of the preliminary analysis TSD. This also includes an engineering questionnaire

5-14
shown in appendix 12A of the preliminary analysis TSD. Likewise, interviews conducted during 

the NOPR analysis are discussed in chapter 5 of the NOPR TSD.

During preparation of the NODA analsis, DOE’s contractor conducted further interviews 

to obtain additional information to support both cost analysis and energy use analysis. All of 

these interviews were conducted under non-disclosure agreements with the manufacturers. 

Hence, none of the individual responses can be reported. However, adjustments to values for 

many of the parameters and costs used in the engineering analysis were based on input from 

these discussions.

5.5.4 Energy Testing

DOE conducted energy testing to verify energy use of several of the ice makers obtained 

for reverse engineering, and to provide information about refrigeration system operating 

conditions during different phases of operation to support energy use modeling. A total of 41 ice 

makers were tested in the preliminary, NOPR, and final rule phases, as listed in Table 5.5.2, 

Table 5.5.3, and Table 5.5.4. The tests were conducted using the DOE test procedure applicable 

during the time of test. For continuous models tested before applicability of the DOE test 

procedure (i.e. during the preliminary analysis), the tests were conducted according to AHRI 

Standard 810-2007 with Addendum 1. During many of the tests, additional data recorded

included refrigeration circuit temperatures, refrigerant pressures, potable water circuit 

temperatures, additional air temperatures (the evaporator compartment, compressor/condenser 

compartment, condenser fan air outlet for air-cooled ice makers), and cooling water outlet 

temperature. 

5.6 MANUFACTURING COST MODELING

5.6.1 Generation of Bills of Materials

Each reverse-engineering teardown resulted in a structured bill of materials (BOM). 

Structured BOMs describe each product part and its relationship to the other parts in the 

estimated order in which manufacturers assembled them. The BOMs describe each fabrication 

and assembly operation in detail, including the type of equipment needed (e.g., presses, drills), 

the process cycle times, and the labor associated with each manufacturing step. The result is a 

thorough and explicit model of the production process, which includes space, conveyor, and 

equipment requirements by planned production level.

The BOMs incorporate all materials, components, and fasteners classified as either raw 

materials or purchased parts and assemblies. The classifications into raw materials or purchased 

parts were based on DOE’s previous industry experience, recent information in trade 

publications, and discussions with high- and low-volume original equipment manufacturers 

(OEMs). DOE also visited manufacturing plants to reinforce its understanding of the industry’s 

current manufacturing practices.

For purchased parts, the purchase price is estimated based on volume-variable price 

quotations and detailed discussions with manufacturers and component suppliers. For fabricated 

parts, the prices of “raw” metals (e.g., tube, sheet metal) are estimated based on 5-year averages 

(see section 5.6.4.3), while all other materials and purchased parts reflect current market costs. 

5-15
The cost of transforming the intermediate materials into finished parts is estimated based on 

current industry pricing. 

5.6.2 Cost Structure of the Spreadsheet Models

The manufacturing cost assessment methodology used is a detailed, component-focused 

technique for calculating the manufacturing cost of a product (direct materials, direct labor, and 

the overhead costs associated with production). The first step in the manufacturing cost 

assessment was the creation of a complete and structured BOM from the disassembly of the units 

selected for teardown. The units were dismantled, and each part was characterized according to 

weight, manufacturing processes used, dimensions, material, and quantity. The BOM 

incorporated all materials, components, and fasteners with estimates of raw material costs and 

purchased part costs. Assumptions about the sourcing of parts and in-house fabrication were 

based on industry experience, information in trade publications, and discussions with 

manufacturers. Interview and plant visits were conducted with manufacturers to add industry 

experience on the methodology and pricing.

The last step was to convert this information into dollar values. To perform this task, 

DOE collected information on labor rates, tooling costs, raw material prices, and other factors. 

DOE assumed values for these parameters using internal expertise and confidential information 

available to DOE contractors. Although most of the assumptions are manufacturer specific and 

cannot be revealed, section 5.6.4.2 discusses the values used for each assumption.

In summary, DOE assigned costs of labor, materials, and overhead to each part, whether 

purchased or produced in-house. DOE then aggregated single-part costs into major assemblies 

(e.g., heat exchanger assembly, potable water system, packaging, controls, fan/motor assembly,

refrigerant circuit, wiring harnesses) and summarized these costs in a worksheet. 

5.6.3 Cost Model and Definitions

Once DOE disassembled selected units, gathered information from manufacturer catalogs 

on additional products, and identified technologies, DOE created an appropriate manufacturing 

cost model that could translate physical information into MPCs. The cost model is based on 

production activities and divides factory costs into the following categories:

• Materials: purchased parts (compressor, fan motors, control boards, etc.), raw 

materials (cold rolled steel, copper tube, etc.), and indirect materials that are used for 

processing and fabrication.

• Labor: fabrication, assembly, indirect, and supervisor labor. Fabrication and assembly 

labor costs are burdened with benefits and supervisory costs.

• Overhead: equipment, tooling, and building depreciation, as well as utilities, 

equipment and tooling maintenance, insurance, and property taxes.

5-16
5.6.3.1 Cost Definitions

Because there are many different accounting systems and methods to monitor costs, DOE 

defined the factory cost terms as follows:

• Direct material: purchased parts (out-sourced) plus manufactured parts (made inhouse from raw materials);

• Indirect material: material used during manufacturing (e.g., welding rods, adhesives);

• Fabrication labor: labor associated with in-house piece manufacturing;

• Assembly labor: labor associated with final assembly;

• Indirect labor: labor costs that scaled with fabrication and assembly labor—including

the cost of technicians, manufacturing engineering support, stocking, etc. that were 

assigned on a span basis;

• Equipment and plant depreciation: money allocated to pay for initial equipment 

installation and replacement as the production equipment wears out;

• Tooling depreciation: cost for initial tooling (including nonrecurring engineering and 

debugging of the tools) and tooling replacement as tools wear out;

• Building depreciation: money allocated to pay for the building space and the 

conveyors that feed and/or make up the assembly line;

• Utilities: electricity, gas, telephones, etc.;

• Maintenance: money spent annually on maintaining tooling and equipment;

• Insurance: appropriated as a function of unit cost; and

• Property tax: appropriated as a function of unit cost.

5.6.4 Cost Model Assumptions Overview

As discussed in the previous section, assumptions about manufacturer practices and cost 

structure were important to the final product cost estimate. 

In converting physical information about the product into cost information, DOE 

reconstructed manufacturing processes for each component using internal expertise and 

knowledge of the methods used by the industry. DOE used assumptions regarding the 

manufacturing process parameters (e.g., equipment use, labor rates, tooling depreciation, and 

cost of purchased raw materials) to determine the value of each component. DOE then summed 

the values of the components into assembly costs and, finally, the total product cost. The product 

cost included the material, labor, and overhead costs associated with the manufacturing facility. 

The material costs included both direct and indirect materials. The labor costs included 

5-17
assembly, fabrication, supervisor, and indirect labor. Overhead costs included equipment 

depreciation, tooling depreciation, building depreciation, utilities, equipment, tooling 

maintenance, insurance, property, and taxes.

DOE used the information gathered from manufacturer interviews and factory visits to 

help in development of the cost model.

The following sections discuss specific assumptions about outsourcing, factory 

parameters, production volumes, and material prices. Manufacturer-specific assumptions are 

presented as industry averages to prevent disclosure of confidential information.

5.6.4.1 Fabrication Estimates

DOE characterized parts based on whether manufacturers purchased them from outside 

suppliers or fabricated them in-house. For purchased parts, DOE estimated the purchase price. 

For fabricated parts, DOE estimated the price of raw materials (e.g., tube, sheet metal) and the 

cost of transforming them into finished parts. Whenever possible, DOE obtained price quotes 

directly from the manufacturers’ suppliers.

DOE based the manufacturing operations assumptions on internal expertise, interviews 

with manufacturers, and manufacturing facilities site visits. The major manufacturer processes 

identified and developed for the spreadsheet model are listed in Table 5.6.1. Fabrication process 

cycle times were estimated and entered into the BOM. 

Table 5.6.1 Cost Model In-House Manufacturing Operation Assumptions

Fabrication Finishing Assembly/Joining Quality Control

Fixturing

Stamping/Pressing

Turret Punch

Tube Forming

Machine

Cutting & Shearing

T-Drill

Tube/Wire Bending

Brazing

Vacuum Forming

Blow Molding

Washing

Painting

Powder Coating

De-burring

Polishing

Refrigerant Charging 

Adhesive Bonding

Spot Welding

Seam Welding

Packaging

Inspecting & Testing

5.6.4.2 Factory Parameters Assumptions

DOE used information gathered from publicly available literature, manufacturer 

interviews, and analysis of common industry practices to formulate factory parameters for each 

type of manufacturer. DOE first made assumptions about a set of preliminary factory parameters 

before the manufacturer interviews. DOE then revised the assumptions using comments and 

information gathered during the interviews. Table 5.6.2 lists DOE’s assumptions for ice maker

manufacturers.

During manufacturer interviews in the NOPR analyses, DOE found that some larger 

batch equipment classes were produced in much smaller volumes than other batch equipment 

5-18
classes. To more accurately capture the costs of these products, DOE created a cost model based 

on low production volumes for batch products.

Table 5.6.2 ACIM Factory Parameter Assumptions

Parameter Batch

(high-volume)

Batch

(low-volume) Continuous

Plant Capacity (units/year) 25,000 5,000 6,000

Actual Annual Production Volume (units/year) 12,000 2,500 3,000

Fabrication Labor Wages ($/hour) 16.00 16.00 16.00

Fringe Benefits Ratio 50% 50% 50%

5.6.4.3 Material Cost Assumptions

DOE determined the cost of raw materials using publicly available information such as 

the American Metals Market,

1 interviews with manufacturers, and direct discussions with 

material suppliers. Common metals used in the fabrication of ice makers include cold rolled steel 

(CRS), stainless steel, copper tubing, and aluminum. There have been large fluctuations in metal 

prices over the last few years. To account for these fluctuations, DOE used a 5-year average of 

metal prices from the Bureau of Labor Statistics Producer Price Indices (PPIs) spanning from 

2008 to 2013 with an adjustment to 2013 dollars (2013$).

2 DOE used the PPIs for copper rolling, 

drawing, and extruding and steel mill products, and made the adjustments to 2013$ using the 

gross domestic product implicit price deflator.3 For resins used in the fabrication of these 

refrigeration products, DOE used current resin prices gathered from industry research, 

publications such as Plastics News,

4 and interviews with manufacturers.

5.6.5 Manufacturing Production Cost

After finalizing the cost estimate for each teardown unit, DOE prepared a detailed 

summary for relevant components, subassemblies, and processes. The BOM thus details all 

aspects of product costs. DOE totaled the cost of materials, labor, and direct overhead used to 

manufacture a product in order to calculate the manufacturing production cost.c Figure 5.6.1

shows the general breakdown of costs associated with manufacturing a product. 

The full cost of the product is equal to the manufacturer selling price (MSP) minus 

profits. The full cost includes the MPC, and the non-production cost. 

c When viewed from the companywide perspective, the sum of all material, labor, and overhead costs equals the 

company’s sales cost, also referred to as the cost of goods sold (COGS).

5-19

 
Figure 5.6.1 Full Production Costs

5.6.6 Incremental Cost Estimates

Incremental costs were determined for design options and are applied to the estimated 

cost of the baseline-efficiency ice makers. The approach for estimating the incremental costs 

varied, depending on the design option. Details in this calculation that are specific to individual 

design options are discussed in section 5.10.2. Aspects of the incremental cost calculation that

were generally applied to multiple design options are discussed in this section.

Many of the design options involve replacement of a current component with a higher

efficiency component. For these design options, the increased price paid by the OEM for the new 

component represents the manufacturing cost increase—other elements of product cost such as 

overhead and capital expenditures would be insignificantly affected by these design changes. 

The appropriate price increases are discussed in section 5.10.2 by design option.

For some design changes, calculating the cost impact of the design change required direct 

use of the manufacturing cost model to determine changes to a number of parts. The baseline 

manufacturing cost was subtracted from the manufacturing cost of the modified design to 

determine the incremental cost of the design option. This approach was used in particular for 

condenser size increases.

Manufacturer 

Selling Price

Full Cost of 

Product

Manufacturer 

Production Cost

Direct 

Labor

Direct 

Material Overhead

Indirect Labor

Indirect Material

Maintenance

Depreciation

Taxes

Insurance related 

to assets

Non-Production 

Cost

Selling

Market 

Research

Advertising

POS Promotion

Salesperson 

Comp & Travel

Logistics 

(Warehousing, 

Delivery, RecordKeeping, Order 

Processing)

General & 

Admin.

Costs of 

Service & 

Staff Units

General 

Corporate 

Costs 

(Comp, etc)

R & D

Costs associated 

with finding new 

and improved 

products or 

processes

Code 

Compliance

Modernization

Interest

Cost of 

borrowing 

funds

Profit

5-20
5.6.6.1 G&A and Profit

DOE estimated the further addition to the MSP associated with general and 

administrative costs (G&A) and profit for the appliance industry as 25 percent of MPC. This 

adder was applied to all of the MPC estimates in order to determine MSP numbers. This markup 

is described in more detail in section 5.7.

5.7 MANUFACTURER MARKUP

Manufacturers and dealers apply a markup to cover their operating costs and profit 

margins in manufacturing and distribution. In the engineering analysis, DOE determined a 

manufacturer markup and applied it to the MPC to arrive at the MSP. DOE is maintaining the 

estimated 1.25 manufacturer markup factor in the final rule, as utilized in both the preliminary 

and NOPR analyses.

5.8 ENERGY USE CALCULATIONS

5.8.1 Energy Use Calculation Overview

DOE calculated the energy use of ice makers representative of the analyzed equipment 

classes, both as built and using alternative designs, to estimate the energy savings potential of 

design options. Reverse engineering of these products, as described in Section 5.5.2, provided 

the basis for these calculations. Using the estimates of energy use and manufacturing cost for 

these designs, DOE developed incremental cost estimates for multiple efficiency levels for all of 

the analyzed classes.

In response to both public and confidential comments and discussions, DOE adjusted its 

energy use analysis. DOE obtained additional data through equipment testing, as well as 

discussions conducted by DOE’s contractor with manufacturers under non-disclosure 

agreements. Therefore, the current energy analysis is a synthesis of data from several sources, 

including reverse engineering, energy testing (Section 5.5.4), manufacturer interviews, and 

simulation using the FREEZE software. FREEZE simulates the ice making process for both 

batch and continuous units and is described in more detail in Appendix 5A as well as chapter 5 

of the preliminary analysis TSD. 

During the NOPR analysis, DOE made several changes to its analysis of ice maker 

energy use, including, for batch ice makers:

• adjusting the compressor performance calculations—this adjustment involved 

assuming the compressor delivers less refrigerant flow than projected by compressor 

vendor performance data, with no reduction in power input;

• using data from tests of ice makers to model the increase of harvest energy as 

condensing temperature decreases;

• considering reduction in potable water use down to 20 gallons per 100 lb ice for ice 

maker designs using more than this amount of potable water; and

• incorporating energy use reduction for drain water heat exchangers used in batch 

equipment.

5-21
The NOPR analysis also considered use of larger water-cooled condensers, which had not 

been considered during the preliminary analysis.

For the final rule, DOE made additional changes to the energy use anlaysis, based on 

public comments and confidential data received in discussions with manufacturers. These 

adjustments are further discussed in section 5.10.2 and included:

• review of maximum-available ice makers used for calibration of energy use 

calculations for maximum-available design combinations, leading to adjustment of 

some of the maximum available efficiency levels;

• a data-based approach for calculating the energy use savings associated with increase 

in compressor EER;

• considering sizes for water-cooled condensers that represent standard sizes available 

from vendors;

• adjusting the efficiency assumptions for ECM motors;

• elimination of some design options that increase package size for specific classes.

5.8.2 Application of Design Options and Calibrations

To guide the application of design options in creating cost-efficiency curves, DOE

calibrated its analysis using data gathered from purchased commercial products. For a given 

equipment class, DOE compared the calculated efficiency of a given design with energy use 

ratings and/or energy test data for high efficiency ice makers within the same equipment class. 

DOE used such comparisons to calibrate the energy use calculations associated with the 

maximum available technology level that currently exists in units available on the market. 

For example, consider the IMH-A-Small-B class, where DOE inspected ice makers at 

both the baseline and max available efficiencies, namely 0.1% and 19.3% below the current 

standards, respectively. These inspections showed the following differences in the maximum

available efficiency unit as compared with the baseline unit:

• increases in both the evaporator face area and condenser volume, and an increase in 

the chassis size to accommodate these growths

• an increase in condenser fan size and a change from a shaded pole motor (SPM)

motor to a permanent split capacitor (PSC) motor

• an increase in compressor energy efficiency ratio (EER)

Through this process, the energy use analysis projected energy use for ice maker designs 

that comprise all applied design options. Subsequently, to create a cost-efficiency curve, these 

design options are then ordered sequentially according to their respective impact on energy use 

reduction on a per unit cost basis. For the IMH-A-Small-B equipment class, DOE selected the 

following design options to increase efficiency from baseline to 25.5 percent below baseline, as 

shown in Table 5.8.1.

Table 5.8.1 IMH-A-Small-B Design Options

% Below Baseline Design Option

0.0 Baseline

5-22
1.0 Increase Condenser Width by 1.3"

4.8 Increase Compressor EER from 4.86 to 5.20

6.5 Switch to PSC Condenser Fan Motor

8.5 Switch to ECM Condenser Fan Motor

21.3 Increase Evaporator Area by 51% (with chassis growth)

22.5 Switch to ECM Pump Motor

25.5 Drain Water Heat Exchanger

The maximum level of efficiency reached may not be equivalent to that of the maximum 

available unit. In some cases, DOE inspected multiple high efficiency units in order to gain 

additional insight into appropriate design options found in the most efficient units. Furthermore, 

DOE considered design options that were different from the max available unit, based on other 

available data. For example, when more efficient compressor options were available, DOE may 

have selected a higher compressor EER design option than the maximum available unit 

inspected. Additionally, DOE may have selected more efficient motors than those present in the 

maximum available units, based on data supporting its engineering feasibility. DOE did not 

consider chassis sizes larger than those available on the market.

In selecting its design options, DOE calibrated each cost-efficiency curve against the 

maximum available efficiency. Specifically, DOE aimed to select a set of design options, 

exclusive of ECMs and DWHXs, in order to reach the energy efficiency level of the maximum 

available unit, and to represent designs that are consistent with the maximum available units.

Both ECMs and DWHXs were excluded from the calibration, as DOE determined that these 

technologies are not commonly used in current ice makers. In the current example, DOE chose 

all design options for the IMH-A-Small-B class, except for those involving ECMs and DWHXs, 

such that the analysis representing design options used in maximum-available units predicts 

energy use equal to the maximum available efficiency level, i.e. 19.3% below baseline. DOE 

adopted this methodology to ensure selection of design options for energy use analysis that 

reflect designs that currently exist in the market. After the NOPR phase, DOE obtained

additional information through rulemaking comments, additional testing, and confidential 

information exchange between DOE’s contractor and manufacturers. These additional data 

allowed more precise calibration in the final rule analysis than DOE was able to achieve in the 

NOPR analysis.

5.9 DESIGN OPTIONS

After conducting the screening analysis described in chapter 4, DOE considered the 

remaining technologies in the design option analysis. Table 5.9.1 lists the design options DOE 

considered for each equipment class. Some design options are applicable only to certain types of 

equipment. Following the table is a description of how DOE applied each of the design options 

during the engineering analysis. See chapter 3 for background descriptions of the technologies.

5-23
Table 5.9.1 Design Options by Equipment Class

Ice Maker Type Equipment 

Class

Higher-EER Compressor Higher-Efficiency Condenser Fan MotorHigher-Efficiency Pump MotorHigher-Efficiency Auger MotorLarger Air-Cooled CondenserLarger WaterCooled CondenserBatch Fill

Batch

IMH-W-B   

*

IMH-A-B    

RCU-B    

SCU-W-B   

SCU-A-B    

Continuous

IMH-W-C  

IMH-A-C    

RCU-C    

SCU-W-C  

SCU-A-C    

*Used in a few limited cases where this design option was applicable. This design option also included 

reduction of potable water use to 20 gal/100 lb ice, if the potable water use for the analyzed ice maker was 

greater than this level.

5.9.1 Design Options That Were Not Considered for Further Analysis

5.9.1.1 Increased Condenser Water Flow

In the preliminary analysis, DOE proposed an approach for balancing energy and 

condenser water use in the engineering analysis that considers the cost implications of changing 

condenser water use, while maintaining the rulemaking’s focus on energy use reduction. DOE 

proposed using appropriate representative values for water and energy costs, product lifetime, 

and discount rates to calculate a representative LCC for baseline and modified design 

configurations as part of the engineering analysis. In this way, the engineering analysis would 

develop a relationship between energy efficiency and manufacturing cost as is customary in 

engineering analyses (i.e., the cost-efficiency curves), but the ordering of different design 

configurations in this curve would be based on minimizing the representative LCC calculated for 

the candidate design configurations at each successive efficiency level. This approach would 

have provided a way to simultaneously consider the costs of both electricity and water in 

development of the engineering analysis.

To obtain data for developing a relationship between increased condenser water flow rate 

and energy consumption, DOE tested three water-cooled ice makers, testing each multiple times

with varying condensing water flow to evaluate the potential for energy savings and the costeffectiveness of increasing condenser water use. The results of this evaluation are shown in 

Table 5.9.2 through Table 5.9.4. The analysis assumed that in the field, half of the ice makers 

would be used in open systems and half in closed-loop systems. In open systems, the water is 

drained after passing through the ice maker, hence system water consumption is equal to the flow 

of water through the ice maker. In closed-loop systems the water that flows through the ice 

maker is cooled in a cooling tower or fluid cooler and recirculates back to the ice maker. DOE’s 

5-24
analysis assumed use of a cooling tower and calculated water evaporation based on a heat 

removal rate of 1,000 Btu/lb water.

DOE contracted with an independent third-party test lab to conduct the tests. The test lab 

varied the water flow levels by adjusting the valves controlling condenser water flow. 

Table 5.9.2 Condenser Water Flow Test Data for a Water-Cooled IMH Batch Unit

Test 1 Test 2 Test 3

Average Condensing Temperature 

 ̊F 97 107 111

Harvest Capacity

lb/24 hours 375 361 355

Energy Consumption 

kWh/100 lb 4.67 5.13 5.28

LCC Operating Cost

$/100 lb $1.75 $1.38 $1.32

Condenser Water Use

gal/100 lb 165.4 106.5 94.1

Table 5.9.3 Condenser Water Flow Test Data for a Water-Cooled SCU Continuous Unit

Test 1 Test 2 Test 3

Condensing Temperature 

 ̊F 103 107 114

Harvest Capacity

lb/24 hours 515 514 491

Energy Consumption 

kWh/100 lb 3.81 3.89 4.17

LCC Operating Cost

$/100 lb $1.21 $1.05 $1.00

Condenser Water Use

gal/100 lb 106.3 81.7 71.1

Table 5.9.4 Condenser Water Flow Test Data for a Water-Cooled IMH Continuous Unit

Test 1 Test 2 Test 3

Condensing Temperature 

 ̊F 97 108 118

Harvest Capacity

lb/24 hours 654 618 521

Energy Consumption

kWh/100 lb 3.65 4.03 4.51

LCC Operating Cost

$/100 lb $1.10 $0.95 $0.91

Condenser Water Use

gal/100 lb 92.3 64.9 52.2

The analysis shows that increasing condenser water flow would increase cost for all three 

units, even for the first step in water use increase/energy use decrease. DOE concludes that this is 

not a cost-effective way to reduce energy use. As a result, DOE did not comprehensively 

evaluate this design option for water-cooled equipment classes.

5-25
5.9.2 Improved Compressor Efficiency

DOE considered the substitution of higher efficiency compressors for all equipment 

classes. DOE acquired compressor performance data from compressor manufacturers for use in 

the energy analysis, including capacity and power input for scroll and reciprocating compressors 

with capacities of 1,000 to 36,000 British thermal units per hour (Btu/h) rated at AHRI 540 

medium temperature testing conditions. Table 5.9.5 lists the AHRI 540 medium temperature 

operating test conditions. DOE selected compressors for substitution in its analysis with similar 

capacities to those found in baseline units so as to isolate the impact of compressor efficiency.

Table 5.9.5 AHRI 540 Medium Temperature Compressor Rating Test Conditions

Condensing Temperature 120 oF

Evaporating Temperature 20 oF

Suction Temperature 40 oF

Subcooling 0 oF

During the NOPR phase, DOE updated the compressor performance database with new 

compressors and solicited further feedback from stakeholders on the acceptability of certain 

compressor lines. DOE did not consider in its analysis compressors that manufacturers indicated 

are unsuitable for use in ice makers. Issues associated with compressors include the following:

• Some compressor families used a direct suction design that has a large impact on 

compressor reliability in batch-type ice maker applications, due to the requirements of 

the harvest period.

• Some compressor families have had reliability issues for both continuous and batch 

machines and thus present utility issues for their use.

• Some compressor families have unacceptable levels of noise and/or vibrations for this 

application, again presenting utility issues.

 

In its analysis of ice maker energy use in the final rule, DOE adopted a data-based 

approach to estimate the impact on energy usage of increases in compressor efficiency. Based on 

confidential test data collected from manufacturers under non-disclosure agreements and test 

data provided in comments, DOE calculated an average percentage ice maker energy use 

reduction equal to 57% of the percentage compressor energy use reduction. Hence, the energy 

use reduction for the ice maker was calculated as follows.

PP IIIIII MM MM EE EEEE UU RR RR RR = 57% × (1 − EEEEEEbbbbbbbbbbbbbbbb

EEEEEE iiiiiiiiii ii

)

5.9.2.1 Compressor Cost Data

During the preliminary analysis, DOE developed a cost correlation for mediumtemperature compressors as part of the commercial refrigeration equipment (CRE) rulemaking, 

relating the compressor cost to its capacity. DOE assigned the cost derived using the cost 

correlation to median-EER compressors of a given capacity. The cost curve applicable for 

median-EER compressors is shown in Figure 5.9.1. The cost curve is based on capacity at 

5-26
modified operating conditions, at 15 °F evaporating temperature and 95 °F condensing 

temperature. On average, the capacity for these conditions is 1.29 times the capacity at the AHRI 

540 rating conditions. For the NOPR analysis, based on feedback from the manufacturers, DOE 

applied an additional markup of 1.76 to the purchased cost of the compressors to arrive at the 

final cost to the manufacturer. This markup was maintained in the final rule analysis.

Figure 5.9.1 Final Rule Cost Curves for Medium-Temperature Compressors Used in 

Commercial Ice Makers 

DOE utilized a modified approach for determining the cost of increasing EER by 

substituting a higher efficiency compressor, based on the preliminary analysis and feedback 

received in the NOPR phase. DOE used two approaches to compute the cost for compressor 

efficiency increase, using the higher of the two in the analysis. The two approaches are explained 

below.

The first approach, cost A, applies a 5-percent cost increase for each 10-percent increase 

in EER, consistent with the approach used in the NOPR analysis. This cost relationship is 

represented by the following exponential equation:

CC AA = BB BB CC × 1.05�llllll1.1�

NNNNNN EEEEEE

OO OO EEEEEE ��

Eq. 5.2

The alternate approach, cost B, is based on applying a 5% increase in compressor cost for 

a future 2% increase in efficiency, i.e., that this 2% increase would exceed the maximum 

efficiency available in currently available compressors. However, rather than projecting 2% EER 

improvement over the best-efficiency compressors currently available, DOE applied the cost to 

the last 2% EER improvement that increases EER to its current maximum level. The calculation 

5-27
also assumes that there is otherwise no cost difference among existing compressors of a given 

capacity. The exponential relationship used for this calculation is as follows:

CC BB = BB BB CC × 1.05�llllll1.02�mmmmmm�

NNNNNN EEEEEE

OO OO EEEEEE ,1.02���

Eq. 5.3

5.9.3 Increased Air-Cooled Condenser Surface Area

The condenser transfers heat from the refrigerant to the ambient air. A larger surface area 

allows the heat transfer to occur more efficiently, allowing the refrigerant circuit to operate with 

a lower condensing temperature. DOE considered an increase in condenser surface area for all 

air-cooled equipment types. In the final rule analysis, DOE considered condenser size increases 

that would result in an increase in chassis size, after reviewing the space available in reverse 

engineered units. This reverse engineering data determined how much condenser growth could 

be possible before increasing overall equipment size. In some cases, DOE determined that no 

size increase was possible. For RCU ice makers, DOE considered condenser size increases that 

required an increase in remote condenser size.

DOE calculated the energy use reduction associated with condenser size increases using a 

hybrid approach based in part on use of the FREEZE model. DOE adjusted the input parameters 

describing the condenser geometry (e.g. the number or length of tube rows, the fin details, etc.),

the air flow rate, the fan motor power input, and the duration and energy use associated with the 

harvest cycle. 

The changes in harvest cycle parameters were based on testing conducted on a watercooled batch ice maker as summarized in Table 5.9.6. Based on these measurements, DOE 

developed linear relationships between percentages of increases in harvest cycle energy use and 

duration as functions of condensing temperature, which DOE used to adjust FREEZE model 

inputs for these parameters. DOE calibrated the approach with test data received during the 

NOPR phase under NDA by DOE’s contractor, for which the condenser of an air-cooled ice 

maker was replaced with a larger condenser. DOE’s analysis predicted the reduced energy use 

associated with this test to within 0.5%.

Table 5.9.6 Batch Ice Maker Evaluation of Harvest Cycle Dependence on Condensing 

Temperature

Test 1 Test 2 Test 3

Average Condensing Temperature 

 ̊F 97 107 111

Harvest Cycle Energy Use

Watt-Hours 21.2 17.9 17.0

Harvest Cycle Duration

seconds 104 81 73

To calculate the adjusted fan motor power input, DOE assumed that for the initial design, 

three-quarters of the total condenser air flow system pressure drop is associated with pressure 

drop through the condenser itself. To calculate the pressure drop associated with a modified 

design, DOE assumed that pressure drop through the condenser is proportional to condenser face 

5-28
velocity. DOE calculated fan motor power input assuming that power input is proportional to air 

flow times pressure drop.

In the final rule analysis, DOE adjusted its condenser coil cost estimates based on 

feedback from stakeholders. DOE calculated the cost associated with condenser size increases

using the manufacturing cost model. The calculated condenser costs increase roughly linearly 

with condenser core volume (finned region face area times fin depth).

5.9.4 Increased Water-Cooled Condenser Surface Area

DOE considered increased size of water-cooled condensers in its analysis. Water-cooled 

condensers are coaxial-tube heat exchangers in which water flows inside an inner tube and 

refrigerant condenses in the annular space between the inner and outer tubes. The tube assembly 

is generally wrapped in a circular or oval pattern to minimize the required dimensions of the ice 

maker package. The amount of condenser heat transfer surface area can be characterized by the 

length of the unwrapped tube and its diameter. In the final rule analysis, DOE considered 

condenser sizes corresponding to the standard sizes offered by suppliers. 

DOE used the FREEZE model with adjustment of condensing temperature and harvest 

cycle input parameters to calculate the energy use reductions associated with increase in watercooled condenser size. Section 5.9.3 discusses the adjustment of the harvest cycle parameters. 

The model uses condensing temperature as an input, based on the typical practice of controlling 

condenser water flow with a regulating valve designed to maintain a specific condensing 

pressure. DOE related condensing temperature to condenser size using a simplified calculation in 

which the transferred load is set equal to the UA-value (overall heat transfer coefficient times 

surface area) time the log-mean temperature difference calculated assuming that the refrigerant 

temperature is equal to the condensing temperature throughout the condenser length. This 

approach underestimates the required surface area because it doesn’t take into consideration the 

increased temperature difference between the refrigerant and the water in the desuperheating 

region of the condenser.

5.9.5 Evaporator Size Increase

The evaporator transfers heat from the freezing liquid water to the refrigerant. Larger 

surface area allows the heat transfer to occur more efficiently, allowing the refrigeration system 

to transfer the same amount of heat with a higher evaporating temperature. In the final rule

analyses, DOE considered an increase in evaporator surface area for the IMH-A-Small-B, IMHW-Small-B, and SCU-W-Large-B equipment classes. In many batch classes, DOE concluded 

that space for increased evaporator size would not be available within the ice maker package 

size. DOE did not consider evaporator size increase for continuous classes. DOE calculated 

energy use reductions associated with this design option using the FREEZE model by increasing 

the input UA value (heat transfer coefficient times effective evaporator surface area, with overall 

units of Btu/h-°F) consistent with the surface area increase. DOE also increased the input 

parameters for harvest cycle energy use and harvest cycle time proportional to the surface area 

increase, to account for the larger size of evaporator required to be warmed during the harvest 

cycle. In the final rule analysis, DOE doubled its NOPR-phase cost estimates associated with 

increased evaporator size, based on confidential discussions with manufacturers. 

5-29
With application of evaporator size increase that necessitates increase in package size, 

DOE also added additional shipping cost associated with the larger packages. 

5.9.6 Design Options that Require Chassis Size Increases

DOE has including package-size-increasing design options in its final rule engineering 

analysis for a limited number of equipment classes, specifically IMH-A-Small-B, IMH-WSmall-B, and IMH-A-Large-B. For the last of these classes, DOE considered package size 

increase only for the analysis at 800 lb/24 hours representative capacity. In response to 

comments received in the NOPR phase, DOE developed separate analyses for 22 inch wide units 

of the IMH-A-Small-B, IMH-W-Small-B, and IMH-A-Large-B classes, for DOE did not 

consider package size increase. DOE also considered size increase for the remote condensers of 

RCU classes. DOE only considered package size increase where the representative baseline unit 

had space to grow relative to the largest units on the market with similar harvest capacity within 

the given class—DOE limited the growth so that final equipment sizes would be consistent with 

these largest market sizes. The equipment growth allowed for consideration of larger heat 

exchangers to increase equipment efficiency. 

Table 5.9.7 shows the maximum sizes DOE considered in the analysis. As mentioned, the 

new chassis sizes are consistent with sizes of current units of the given classes that are 

commercially available.

Table 5.9.7 Description of Ice Making Head Size Increases in the Engineering Analysis

Equipment class Size

Descriptor

Height

inches

Width

inches

Depth

inches

Volume

cubic feet

IMH-A-Small-B Baseline 17 30 25 7.0

Growth 22 30 25 9.1

IMH-A-Large-B (800 lb/24 hours

representative harvest capacity)

Baseline 26 30 24 10.8

Growth 29 30 24 12.1

IMH-W-Small-B Baseline 20 30 24 8.3

Growth 22 30 25 9.1

DOE initially calculated costs associated with chassis size growths in the NOPR phase 

using the updated cost model and added these costs to the incremental costs associated with heat 

exchanger growths in these equipment classes. For the IMH-A-Small-B and IMH-W-Small-B 

classes, the size increases were associated with evaporator size increases. As described in section 

5.9.5, DOE doubled the overall cost increases for this design option (including the cost 

associated with the chassis size increase). For the IMH-A-Large-B class, the package size 

increase was associated with an increase in height of the condenser. For the final rule analysis, 

DOE adjusted costs associated with the chassis and the condenser based on discussions with 

manufacturers. 

5.9.7 Condenser Fan Motors

A condenser fan in a commercial ice maker draws in ambient air and circulates it through 

the condenser coils and over the compressor shell, thereby cooling the condensing refrigerant 

and the compressor. The motors used to run these fans are typically shaded pole (SP) or

permanent split capacitor (PSC) motors with shaft power outputs ranging from 3 to 750 W,

5-30
depending on the ice maker’s harvest capacity. DOE considered use of higher-efficiency motors, 

specifically upgrade from SP to PSC or to electronically commutated (ECM) motors.

In the final rule analyses, DOE adjusted its assumptions for motor efficiencies as 

compared with the NOPR and preliminary analyses. DOE also updated costs associated with 

these motors based on stakeholder feedback. In the final engineering analysis, DOE assumed that 

the efficiencies of the three motor types are 35% for SP, 45% for PSC, and 66% for ECMs. DOE 

also assumed that the relative efficiencies of these motor types are constant through range of 

shaft power considered. For example, at a lower shaft power level, a shaded power motor may 

have an efficiency of 25%. The efficiency of a replacement standard-efficiency PSC motor 

would have been calculated as 25% ÷ 35% × 45% = 32%.

DOE’s final rule cost assumptions for the different motor types as a function of shaft 

output power are summarized in Table 5.9.8.

Table 5.9.8 Motor Cost Information Use for Final Rule Engineering Analysis

Shaft Power 

W Horsepower

Costs by Motor Type

$

Shaded Pole Permanent 

Split Capacitor

Electronically 

Commutated

6 0.008 $12 $20 $50 

9 0.012 $14 $24 $48 

12 0.016 $16 $27 $47 

25 0.034 $21 $35 $47 

37 0.050 $23 $39 $49 

60 0.080 $26 $45 $57 

100 0.134 $30 $50 $76 

200 0.268 $34 $58 $130 

373 0.500 $38 $65 $229 

560 0.750 $41 $82 $326 

746 1.000 $43 $102 $420 

5.9.8 Auger Motors

Augers are integrated into the evaporators of continuous commercial ice makers, as 

discussed in chapter 3 of the final rule TSD. Auger assemblies incorporate a motor and a gear 

drive to reduce the motor speed to that of the auger. The motors typically used for augers in ice 

makers using single-phase power are PSC or capacitor start induction run (CSIR) motors with 

shaft power output between 200 and 400 W. 

For the final rule engineering analysis, DOE considered conversion to an ECM motor to 

improve efficiency.d DOE’s analyses involved auger motors with shaft power of 1⁄4 and 1⁄2 

horsepower with corresponding baseline costs of $315 and $400, respectively. DOE further 

assumed that the motor cost would double when switching to a ECM motor.

d Throughout much of the TSD and Final Rule, DOE discusses these motors as electronically commutated motoros 

(ECMs). However, the term ECM implies that the motor uses direct current (DC) power. DOE has used the term 

ECM to refer more broadly to PMDC motors that may be driven using DC or three phase power. 

5-31

 
5.9.9 Pump Motors

Water pumps are used in batch commercial ice makers to assist in draining water from 

the water sump during the harvest cycle and to circulate sump water back to the evaporator 

during the freeze cycle. The motors used for these pumps are generally shaded pole motors with 

shaft power outputs between 6 and 25 W. In the final rule analyses, DOE used the updated 

information collected for condenser fan motors to evaluate pump motor design options, assuming 

that this information is also representative of the potential for performance improvement and cost 

increase for pump motors (see section 5.9.7 for details).

5.9.10 Drain Water Heat Exchanger

In the NOPR analysis, DOE reconsidered its preliminary suggestion that external drain 

water heat exchangers cannot be considered part of an ice maker simply because they are not 

specifically mentioned in the EPCA definition, and concluded that they can be considered as a 

design option and to be part of a basic model ice maker, assuming that the drain water heat 

exchanger is sold and shipped with the unit and that the installation and operating instructions 

clearly reinforce this inclusion by detailing the installation requirements for the heat exchanger.

As a result, DOE accounted for the use of a drain water heat exchanger as a design option in 

batch ice makers. DOE’s final rule analysis also includes consideration of drain water heat 

exchangers for batch ice makers.

DOE conducted testing of a commercially available drain water heat exchanger with a 

batch water-cooled IMH unit to confirm the efficiency improvement possible with this design 

option. The test results are shown in Table 5.9.9.

Table 5.9.9 Energy Consumption Tests with a Drain Water Heat Exchanger

Baseline Test With Drain Water 

Heat Exchanger

Average Condensing Temperature 

 ̊F 97 96

Harvest Capacity

lb /24 hours

375 384

Energy Consumption 

kWh/100 lb 4.67 4.49

Percent Reduction in Energy 

Consumption from Baseline 0.0% 3.9%

When calculating the energy use reduction associated with this design option, DOE 

reduced the energy use of the previous design configuration by 3.9 percent, resulting in savings 

that are less than 3.9 percent of the baseline energy use.

DOE conducted a teardown on the purchased drain water heat exchanger and also 

interviewed several component manufacturers. DOE noted that some parts of the drain water 

heat exchangers are considered proprietary and protected by patent. Because of this, DOE 

evaluated the drain water heat exchanger as a purchased part—not as a component assembled 

from parts fabricated within the factory. As a result, the estimated cost for this design option is 

relatively high, being based on vendor pricing information. DOE used a cost of $270 for a larger 

9-inch-diameter version of the heat exchanger (for products with harvest capacity greater than 

5-32
1,000 lb/24 hours) and a cost of $210 for a smaller, 7-inch-diameter version of the heat 

exchanger (for products with harvest capacity less than 1,000 lb/24 hours). DOE also considered 

that two drain water heat exchangers could be attached in series for ice makers with harvest 

capacity greater than 2,000 lb/24 hours.

5.9.11 Batch Water Fill and Reduced Potable Water Flow for Batch-Type Ice Makers

In the final rule analysis, DOE considered use of batch water fill and reductions in 

potable water flow for batch ice makers. With batch water fill, all of the potable water needed 

for the freeze cycle is added to the ice maker sump upon the initiation of a freeze cycle. This 

allows for cooling of all of the water at the start of the freeze cycle when the evaporator is 

relatively warm and the evaporating temperature is higher. Provision of cooling by the 

refrigeration system during this part of the cycle is more efficient, due to the higher evaporating 

temperature. Generally, adopting this approach requires use of a solenoid valve for filling the 

sump, more sophisticated controls, and greater sump volume to contain all of the potable water 

required for a freeze cycle. The greater control system sophistication allows more precise control 

of total potable water use, which DOE considered as part of conversion to batch fill. DOE 

calculated costs for adoption of this design option based on these design changes and assuming 

that electronic controls would be required. Based on feedback from stakeholders, DOE limited 

the decrease in potable water flow to 20 gallons per 100 lb of ice, to ensure proper drainage of 

particulates from the sump. DOE considered this design option only for equipment classes for 

which the baseline unit selected for the analysis did not have a batch-fill system.

5.10 ENGINEERING ANALYSIS RESULTS

This section presents the engineering analysis results, including the incremental cost 

curves. DOE developed cost-efficiency curves for ice maker equipment classes, based on 

combinations of individual design options. DOE subsequently created cost-efficiency curves 

based on selected efficiency levels, as previously defined for each equipment class (see section 

5.4) for simplified downstream analysis. 

5.10.1 Overview

Table 5.10.1 presents MPC, MSP, and water use characteristics for baseline batch ice 

maker configurations that DOE directly analyzed. Table 5.10.2 presents incremental MSP for the 

evaluated efficiency levels for batch ice makers. The representative harvest capacities indicated 

in the tables are those of the products that were the bases of the analyses.

Table 5.10.1 Batch Ice Maker Baseline Characteristics

Equipment Class

Represe

ntative 

Harvest 

Capacity 

lb/24 

hours

MPC MSP

Potable 

Water Use 

gal/

100 lb

Condenser 

Water Use 

gal/

100 lb

IMH-W

Small 300 $898 $1,122 24.0 153

Small (22 inch wide) 300 $898 $1,122 24.0 153

Medium 850 $1,545 $1,931 19.7 153

Large 1,500 $1,709 $2,136 22.3 162

5-33
2,600 $3,748 $4,684 19.7 174

IMH-A

Small 300 $916 $1,144 25.6

NA

Small (22 inch wide) 300 $916 $1,144 18.5

Large 800 $1,494 $1,867 22.7

1,500 $1,652 $2,065 20.0

Large (22 inch wide) 800 $1,494 $1,867 18.0

RCU

Large 1,500 $1,970 $2,463 20.0 NA 2,400 $3,203 $4,003 19.7

SCU-W

Large 300 $1,224 $1,530 30.0 132

SCU-A

Small 110 $1,051 $1,314 34.0 NA Large 200 $1,121 $1,402 30.0

Table 5.10.2 Incremental Manufacturer Selling Price Results for Batch Ice makers

Equipment 

Class

Max 

Available

Representative 

Harvest 

Capacity 

lb/24 hours

EL2* EL3 EL4 EL5 EL6

Max Tech

Level Cost

IMH-W

Small 19.1% 300 $44 $79 $145 23.9% $373

Small 

(22 inch wide) 16.9% 300 $49 $87 $345 21.5% $363

Medium 14.3% 850 $67 $204 18.1% $410

Large 5% 1,500 8.3% $419

2.5% 2,600 7.4% $526

IMH-A

Small 19.3% 300 $60 $100 $139 $386 25.5% 390

Small 

(22 inch wide) 16.6% 300 $57 $301 18.1% $324

Large 16.1% 800 $45 $92 $158 23.4% $381

6.0% 1,500 $427 11.8% $474

Large 

(22 inch wide) 5.5% 800 $75 $316 15.8% $321

RCU

Large 15.7% 1,500 $59 $254 17.3% $468

14.9% 2,400 $285 13.9% $796

SCU-W

Large 27.6% 300 $45 $69 $93 $121 29.8% $361

SCU-A

Small 31.2% 110 $24 $38 $53 $97 $148 32.7% $367

Large 27.9% 200 $29 $45 $72 $107 29.1% $349 * EL = efficiency level; EL1 is the base line efficiency level while EL2 through EL6 represent increased efficiency levels.

Table 5.10.3 and Table 5.10.4 present the engineering analysis results for continuous ice 

makers. The table presents baseline ice maker condenser water use with adjustment for ice 

quality as required by the test procedure final rule.

5-34
Table 5.10.3 Continuous Ice Maker Baseline Characteristics 

Equipment 

Class

Representative 

Harvest 

Capacity

lb/24 hours

MPC MSP

Condenser 

Water Use 

gal/100 lb

Potable Water 

Use gal/100 lb

IMH-A

Small 310 $2,706 $3,381 NA 12.0

820 $2,090 $2,613 NA 13.6

RCU

Small 800 $2,090 $2,613 NA 12.0

SCU-A

Small 220 $1,411 $1,764 NA 12.0

Table 5.10.4 Incremental Manufacturer Selling Price Results for Continuous Ice Makers

Equipment 

Class

Max 

Available

Representative 

Harvest 

Capacity 

lb/24 hours

EL2* EL3 EL4 EL5 EL6

Max Tech

Level Cost

IMH-A

Small 25.4% 310 $33 $54 $76 $390 25.7% $393

Large 17% 820 $30 $48 $488 23.3% $502

RCU

Small 18.4% 800 $30 $72 $102 $523 26.6% $540

SCU-A

Small 24.4% 220 $14 $29 $53 $377 26.6% $382

* EL = efficiency level; EL1 is the base line efficiency level while EL2 through EL6 represent increased efficiency levels. 

For equipment classes that DOE did not directly analyze (the secondary equipment 

classes), DOE used results of a directly analyzed equipment class to represent the cost-efficiency 

characteristics. Table 5.10.5 shows the associations between directly analyzed and secondary 

equipment classes. These correlations are based on the availability of similar design options for 

similar equipment classes and observation that the range of efficiency levels attained by 

commercially available units are similar. 

Table 5.10.5 Directly Analyzed Equipment Classes Used to Develop Standards for 

Secondary Classes

Secondary 

Equipment Class

Directly Analyzed Equipment Class 

Associated with Secondary Equipment Class

RCU-NRC-Small-B RCU-NRC-Large-B

RCU-RC-Small-B RCU-NRC-Large-B

RCU-RC-Large-B RCU-NRC-Large-B

SCU-W-Small-B SCU-W-Large-B

IMH-W-Small-C IMH-A-Small-C

IMH-W-Large-C IMH-A-Large-C

RCU-NRC-Large-C RCU-NRC-Small-C

RCU-RC-Small-C RCU-NRC-Small-C

RCU-RC-Large-C RCU-NRC-Small-C

5-35
SCU-W-Small-C SCU-A-Small-C

SCU-W-Large-C SCU-A-Small-C

SCU-A-Large-C SCU-A-Small-C

5.10.2 Design Option Analyses

Table 5.10.6 through Table 5.10.24 summarize the engineering analysis at the design 

option level for the directly-analyzed equipment classes.

Table 5.10.6 Manufacturing Cost and Energy Consumption Data for Air-Cooled IMH 

Batch Ice Machines with Capacities Less Than 450 lb/24 hours

Energy 

Consumption 

kWh/100 lb

Percent 

Energy 

Use 

Reduction

Manufacturer Manufacturer

Production Design Options Successively Added

Cost Selling Price 

7.68 0.0% $916 $1,144 Baseline

7.61 1.0% $919 $1,149 Increase Condenser Width by 1.3"

7.31 4.8% $936 $1,169 Increase Compressor EER from 4.86 to 5.20

7.18 6.5% $948 $1,184 Switch to PSC Condenser Fan Motor

7.03 8.4% $963 $1,204 Switch to ECM Condenser Fan Motor

6.05 21.3% $1,065 $1,331 Increase Evaporator Area by 51% (with 

chassis growth)

5.95 22.5% $1,086 $1,358 Switch to ECM Pump Motor

5.72 25.5% $1,305 $1,631 Drain Water Heat Exchanger

kWh = kilowatt-hours; EER = energy efficiency ratio; PSC = permanent split capacitor; ECM = electronically commutated 

motor

Representative Harvest Capacity: 300 lb/24 hours

Table 5.10.7 Manufacturing Cost and Energy Consumption Data for Air-Cooled IMH 

Batch Ice Machines with Capacities Greater Than 450 lb/24 hours (Smaller Sizes)

Energy 

Consumption 

kWh/100 lb

Percent 

Energy 

Use 

Reduction

Manufacturer Manufacturer

Production Design Options Successively Added

Cost Selling Price 

6.01 0.0% $1,494 $1,867 Baseline

5.47 9.0% $1,532 $1,915 Increase Compressor EER from 5.66 to 6.7

5.31 11.7% $1,550 $1,938 Switch to PSC Condenser Fan Motor

5.13 14.7% $1,563 $1,954 Switch to ECM Condenser Fan Motor

5.01 16.7% $1,594 $1,992 Change to Batch Water Fill

4.93 17.9% $1,614 $2,017 Switch to ECM Pump Motor

4.85 19.3% $1,640 $2,049 Increase Condenser Height by 3" (with 

chassis growth)

4.79 20.3% $1,657 $2,071 Increase Condenser Width by 3.25"

4.60 23.4% $1,875 $2,344 Drain Water Heat Exchanger

kWh = kilowatt-hours; EER = energy efficiency ratio; PSC = permanent split capacitor; ECM = electronically commutated 

motor

Representative Harvest Capacity: 800 lb/24 hours

5-36
Table 5.10.8 Manufacturing Cost and Energy Consumption Data for Air-Cooled IMH 

Batch Ice Machines with Capacities Greater Than 450 lb/24 hours (Larger Sizes)

Energy 

Consumption 

kWh/100 lb

Percent 

Energy 

Use 

Reduction

Manufacturer Manufacturer

Production Design Options Successively Added

Cost Selling Price 

5.24 0.0% $1,652 $2,065 Baseline

5.15 1.6% $1,674 $2,092 Switch to PSC Condenser Fan Motor

5.06 3.4% $1,710 $2,138 Switch to ECM Condenser Fan Motor

4.99 4.7% $1,737 $2,171 Increase Compressor EER from 7 to 7.16

4.83 7.8% $1,819 $2,273 Increase Condenser Width by 6", Add One 

Additional Condenser Row

4.81 8.2% $1,839 $2,299 Switch to ECM Pump Motor

5.24 11.8% $2,126 $2,658 Drain Water Heat Exchanger

kWh = kilowatt-hours; EER = energy efficiency ratio; PSC = permanent split capacitor; ECM = electronically commutated 

motor

Representative Harvest Capacity: 1500 lb/24 hours

Table 5.10.9 Manufacturing Cost and Energy Consumption Data for Water-Cooled IMH 

Batch Ice Machines with Capacities Less Than 500 lb/24 hours

Energy 

Consumption 

kWh/100 lb

Percent 

Energy 

Use 

Reduction

Manufacturer Manufacturer

Production Design Options Successively Added

Cost Selling Price 

6.14 0.0% $898 $1,122 Baseline

5.72 6.8% $919 $1,148 Increase compressor EER from 4.59 to 5.2

5.22 15.0% $977 $1,221 Increase Condenser Length by 59%

5.01 18.3% $1,007 $1,259 Change to Batch Water Fill

4.96 19.2% $1,017 $1,271 Increase Evaporator Area by 4.7% (with 

chassis growth)

4.86 20.8% $1,052 $1,315 Switch to ECM Pump Motor

4.68 23.9% $1,271 $1,588 Drain Water Heat Exchanger

kWh = kilowatt-hours; EER = energy efficiency ratio; PSC = permanent split capacitor; ECM = electronically commutated 

motor

Representative Harvest Capacity: 300 lb/24 hours

Table 5.10.10 Manufacturing Cost and Energy Consumption Data for Water-Cooled IMH 

Batch Ice Machines with Capacities between 500 and 1,436 lb/24 hours

Energy 

Consumption 

kWh/100 lb

Percent 

Energy 

Use 

Reduction

Manufacturer Manufacturer

Production Design Options Successively Added

Cost Selling Price 

4.65 0.0% $1,545 $1,931 Baseline

4.40 5.2% $1,566 $1,958 Increase Compressor EER from 5.63 to 6.2

4.27 8.1% $1,584 $1,980 Increase Compressor EER from 6.2 to 6.7

4.19 9.7% $1,604 $2,005 Switch to ECM Pump Motor

3.96 14.8% $1,736 $2,170 Increase Condenser Length by 72%

3.80 18.1% $1,955 $2,443 Drain Water Heat Exchanger

kWh = kilowatt-hours; EER = energy efficiency ratio; PSC = permanent split capacitor; ECM = electronically commutated 

motor

Representative Harvest Capacity: 850 lb/24 hours

5-37
Table 5.10.11 Manufacturing Cost and Energy Consumption Data for Water-Cooled IMH 

Batch Ice Machines with Capacities Greater Than 1,436 lb/24 hours (Smaller Sizes)

Energy 

Consumption 

kWh/100 lb

Percent 

Energy 

Use 

Reduction

Manufacturer Manufacturer

Production Design Options Successively Added

Cost Selling Price 

4.00 0.0% $1,709 $2,136 Baseline

3.95 1.3% $1,733 $2,167 Increase Compressor EER from 7 to 7.16

3.82 4.4% $1,821 $2,276 Increase Condenser Size

3.81 4.7% $1,842 $2,302 Switch to ECM Pump Motor

3.67 8.3% $2,128 $2,660 Drain Water Heat Exchanger

kWh = kilowatt-hours; EER = energy efficiency ratio; PSC = permanent split capacitor; ECM = electronically commutated 

motor

Representative Harvest Capacity: 1500 lb/24 hours

Table 5.10.12: Manufacturing Cost and Energy Consumption Data for Water-Cooled IMH 

Batch Ice Machines with Capacities Greater Than 1,436 lb/24 hours (Larger Sizes)

Energy 

Consumption 

kWh/100 lb

Percent 

Energy 

Use 

Reduction

Manufacturer Manufacturer

Production Design Options Successively Added

Cost Selling Price 

4.00 0.0% $3,748 $4,684 Baseline

3.90 2.5% $3,787 $4,734 Increase compressor EER from 6.05 to 6.32

3.85 3.7% $3,826 $4,783 Switch to ECM Pump Motors (2)

3.70 7.4% $4,274 $5,342 Drain Water Heat Exchanger

kWh = kilowatt-hours; EER = energy efficiency ratio; PSC = permanent split capacitor; ECM = electronically commutated 

motor

Representative Harvest Capacity: 2600 lb/24 hours

Table 5.10.13 Manufacturing Cost and Energy Consumption Data for Air-Cooled RCU 

Batch Ice Machines with Capacities Greater Than 1000 lb/24 hours (Smaller Sizes)

Energy 

Consumption 

kWh/100 lb

Percent 

Energy 

Use 

Reduction

Manufacturer Manufacturer

Production Design Options Successively Added

Cost Selling Price 

5.10 0.0% $1,970 $2,463 Baseline

4.93 3.4% $1,984 $2,480 Increase Condenser Height by 3" (with 

condenser growth)

4.65 8.9% $2,010 $2,512 Increase Compressor EER from 6.47 to 7.16

4.56 10.5% $2,039 $2,549 Increase Condenser Width by 5" (with unit 

growth)

4.46 12.5% $2,087 $2,609 Switch to ECM Fan Motor

4.42 13.3% $2,107 $2,634 Switch to ECM Pump Motor

4.38 14.1% $2,142 $2,678 Increase Condenser Width by 6" (with unit 

growth)

4.22 17.3% $2,439 $3,048 Drain Water Heat Exchanger

kWh = kilowatt-hours; EER = energy efficiency ratio; PSC = permanent split capacitor; ECM = electronically commutated 

motor

Representative Harvest Capacity: 1500 lb/24 hours

5-38
Table 5.10.14 Manufacturing Cost and Energy Consumption Data for Air-Cooled RCU 

Batch Ice Machines with Capacities Greater Than 1000 lb/24 hours (Larger Sizes)

Energy 

Consumption 

kWh/100 lb

Percent 

Energy 

Use 

Reduction

Manufacturer Manufacturer

Production Design Options Successively Added

Cost Selling Price 

5.10 0.0% $3,203 $4,003 Baseline

4.81 5.7% $3,246 $4,058 Increase compressor EER from 5.68 to 6.32

4.75 6.9% $3,286 $4,107 Switch to ECM Pump Motors (2)

4.65 8.9% $3,375 $4,219 Increase Condenser Width by 18" (with unit 

growth)

4.55 10.7% $3,561 $4,451 Switch to ECM Condenser Fan Motors (2)

4.39 13.9% $3,998 $4,998 Drain Water Heat Exchanger

kWh = kilowatt-hours; EER = energy efficiency ratio; PSC = permanent split capacitor; ECM = electronically commutated 

motor

Representative Harvest Capacity: 2400 lb/24 hours

Table 5.10.15 Manufacturing Cost and Energy Consumption Data for Air-Cooled SCU 

Batch Ice Machines with Capacities Less Than 175 lb/24 hours

Energy 

Consumption 

kWh/100 lb

Percent 

Energy Use 

Reduction

Manufacturer Manufacturer

Production Design Options Successively Added

Cost Selling Price 

12.84 0.0% $1,051 $1,314 Baseline

12.12 5.6% $1,064 $1,329 Increase condenser width by 2.5”, Add One 

Additional Condenser Row

10.02 22.0% $1,109 $1,387 Increase compressor EER from 3.3 to 4.6 

(includes smaller compressor)

9.83 23.4% $1,117 $1,396 Switch to PSC Condenser Fan Motor

9.42 26.6% $1,147 $1,434 Change to Batch Water Fill

9.13 28.9% $1,169 $1,461 Switch to ECM Pump Motor

8.96 30.2% $1,200 $1,499 Switch to ECM Condenser Fan Motor

8.64 32.7% $1,418 $1,773 Drain Water Heat Exchanger

kWh = kilowatt-hours; EER = energy efficiency ratio; PSC = permanent split capacitor; ECM = electronically commutated 

motor

Representative Harvest Capacity: 110 lb/24 hours

Table 5.10.16 Manufacturing Cost and Energy Consumption Data for Air-Cooled SCU 

Batch Ice Machines with Capacities Greater Than 175 lb/24 hours

Energy 

Consumption 

kWh/100 lb

Percent 

Energy Use 

Reduction

Manufacturer Manufacturer

Production Design Options Successively Added

Cost Selling Price 

9.80 0.0% $1,121 $1,402 Baseline

9.26 5.5% $1,136 $1,420 Increase condenser width by 9"

8.20 16.3% $1,171 $1,463 Increase compressor EER from 4.23 to 5.2

7.62 22.2% $1,201 $1,501 Change to Batch Water Fill

7.50 23.5% $1,213 $1,516 Switch to PSC Condenser Fan Motor

7.35 25.0% $1,228 $1,535 Switch to ECM Condenser Fan Motor

7.23 26.3% $1,251 $1,564 Switch to ECM Pump Motor

6.94 29.1% $1,470 $1,838 Drain Water Heat Exchanger

kWh = kilowatt-hours; EER = energy efficiency ratio; PSC = permanent split capacitor; ECM = electronically commutated 

motor

Representative Harvest Capacity: 200 lb/24 hours

5-39
Table 5.10.17 Manufacturing Cost and Energy Consumption Data for Water-Cooled SCU 

Batch Ice Machines with Capacities Greater Than 200 lb/24 hours

Energy 

Consumption 

kWh/100 lb

Percent 

Energy 

Use 

Reduction

Manufacturer Manufacturer

Production Design Options Successively Added

Cost Selling Price 

7.60 0.0% $1,224 $1,530 Baseline

7.07 7.0% $1,254 $1,568 Change to Batch Water Fill

6.12 19.5% $1,315 $1,644 Increase Evaporator Area by 50%

5.70 25.0% $1,345 $1,681 Increase Condenser Length by 50%

5.54 27.1% $1,366 $1,708 Switch to ECM Pump Motor

5.34 29.8% $1,585 $1,981 Drain Water Heat Exchanger

kWh = kilowatt-hours; EER = energy efficiency ratio; PSC = permanent split capacitor; ECM = electronically commutated 

motor

Representative Harvest Capacity: 300 lb/24 hours

Table 5.10.18 Manufacturing Cost and Energy Consumption Data for Air-Cooled IMH 

Continuous Ice Machines with Capacities Less Than 700 lb/24 hours

Energy 

Consumption 

kWh/100 lb

Percent 

Energy Use 

Reduction

Manufacturer Manufacturer

Production Design Options Successively Added

Cost Selling Price 

9.05 0.0% $2,706 $3,382 Baseline

8.24 9.0% $2,734 $3,417 Increased compressor EER from 4.68 to 5.4

8.03 11.3% $2,744 $3,430 Increased condenser width by 3.5"

7.73 14.6% $2,762 $3,452 Switched to PSC Fan Motor

7.41 18.2% $2,773 $3,466 Switched to ECM Fan Motor

7.2 20.4% $2,784 $3,480 Increase condenser height by 4" (no chassis 

increase)

6.72 25.7% $3,099 $3,873 Switched to ECM Auger Motor

kWh = kilowatt-hours; EER = energy efficiency ratio; PSC = permanent split capacitor; ECM = electronically commutated 

motor

Representative Harvest Capacity: 310 lb/24 hours

Table 5.10.19: Manufacturing Cost and Energy Consumption Data for Air-Cooled IMH 

Continuous Ice Machines with Capacities Greater Than 700 lb/24 hours (Smaller Sizes)

Energy 

Consumption 

kWh/100 lb

Percent 

Energy Use 

Reduction

Manufacturer Manufacturer

Production Design Options Successively Added

Cost Selling Price 

6.60 0.0% $2,090 $2,613 Baseline

5.79 12.3% $2,127 $2,659 Increase Compressor EER from 5.02 to 6.0

5.57 15.7% $2,141 $2,677 Increase Condenser Width by 1.5", 

Increase Tube Rows by 1

5.48 17.0% $2,162 $2,703 Switched to PSC Fan Motor

5.38 18.5% $2,189 $2,736 Switched to ECM Fan Motor

5.06 23.3% $2,592 $3,240 Switched to ECM Auger Motor

kWh = kilowatt-hours; EER = energy efficiency ratio; PSC = permanent split capacitor; ECM = electronically commutated 

motor

Representative Harvest Capacity: 820 lb/24 hours

5-40
Table 5.10.20 Manufacturing Cost and Energy Consumption Data for Air-Cooled SCU 

Continuous Ice Machines with Capacities Less Than 700 lb/24 hours

Energy 

Consumption 

kWh/100 lb

Percent 

Energy Use 

Reduction

Manufacturer Manufacturer

Production Design Options Successively Added

Cost Selling Price 

10.12 0.0% $1,411 $1,764 Baseline

9.18 9.3% $1,423 $1,779 Increased condenser rows to 4 from 3, 

larger motor

8.27 18.3% $1,450 $1,812 Changed compressor EER from 4.7 to 5.5

8.11 19.9% $1,463 $1,829 Switched to PSC Fan Motor

7.93 21.6% $1,476 $1,845 Switched to ECM Fan Motor

7.43 26.6% $1,793 $2,242 Switched to ECM Auger Motor

kWh = kilowatt-hours; EER = energy efficiency ratio; PSC = permanent split capacitor; ECM = electronically commutated 

motor

Representative Harvest Capacity: 110 lb/24 hours

Table 5.10.21 Manufacturing Cost and Energy Consumption Data for Air-Cooled RCU 

Continuous Ice Machines with Capacities Less Than 850 lb/24 hours

Energy 

Consumption 

kWh/100 lb

Percent 

Energy Use 

Reduction

Manufacturer Manufacturer

Production Design Options Successively Added

Cost Selling Price 

6.33 0.0% $2,090 $2,613 Baseline

5.82 8.1% $2,111 $2,639 Increase Compressor EER from 4.75 to 5.4

5.52 12.7% $2,133 $2,666 Switched to PSC Fan Motor

5.19 17.9% $2,170 $2,712 Switched to ECM Fan Motor

4.87 23.1% $2,226 $2,783 Increase Condenser Width by 23.25 inches

4.65 26.6% $2,630 $3,287 Switched to ECM Auger Motor

kWh = kilowatt-hours; EER = energy efficiency ratio; PSC = permanent split capacitor; ECM = electronically commutated 

motor

Representative Harvest Capacity: 110 lb/24 hours

Table 5.10.22 Manufacturing Cost and Energy Consumption Data for Air-Cooled IMH 

Batch Ice Machines with Capacities Less Than 450 lb/24 hours (22 inch Wide)

Energy 

Consumption 

kWh/100 lb

Percent 

Energy Use 

Reduction

Manufacturer Manufacturer

Production Design Options Successively Added

Cost Selling Price 

7.68 0.0% $916 $1,144 Baseline

7.57 1.5% $921 $1,151 Increase Condenser Width by 2"

7.27 5.3% $937 $1,172 Increase Compressor EER from 4.86 to 5.20

7.14 7.0% $949 $1,187 Switch to PSC Condenser Fan Motor

6.99 9.0% $965 $1,206 Switch to ECM Condenser Fan Motor

6.64 13.5% $999 $1,249 Increase Evaporator Area by 18.0% (no 

chassis growth)

6.55 14.7% $1,021 $1,276 Switch to ECM Pump Motor

6.29 18.1% $1,240 $1,549 Drain Water Heat Exchanger

kWh = kilowatt-hours; EER = energy efficiency ratio; PSC = permanent split capacitor; ECM = electronically commutated motor

Representative Harvest Capacity: 300 lb/24 hours

5-41
Table 5.10.23 Manufacturing Cost and Energy Consumption Data for Air-Cooled IMH 

Batch Ice Machines with Capacities Greater Than 450 lb/24 hours (Smaller Sizes, 22 inch 

Wide)

Energy 

Consumption 

kWh/100 lb

Percent 

Energy 

Use 

Reduction

Manufacturer Manufacturer

Production Design Options Successively Added

Cost Selling Price 

6.01 0.0% $1,494 $1,867 Baseline

5.80 3.5% $1,514 $1,893 Increase Compressor EER from 5.42 to 5.78

5.64 6.2% $1,532 $1,915 Switch to PSC Condenser Fan Motor

5.46 9.2% $1,546 $1,932 Switch to ECM Condenser Fan Motor

5.34 11.2% $1,576 $1,970 Change to Batch Water Fill

5.26 12.4% $1,596 $1,995 Switch to ECM Pump Motor

5.06 15.8% $1,815 $2,268 Drain Water Heat Exchanger

kWh = kilowatt-hours; EER = energy efficiency ratio; PSC = permanent split capacitor; ECM = electronically commutated 

motor

Representative Harvest Capacity: 800 lb/24 hours

Table 5.10.24 Manufacturing Cost and Energy Consumption Data for Water-Cooled IMH 

Batch Ice Machines with Capacities Less Than 500 lb/24 hours (22 inch Wide)

Energy 

Consumption 

kWh/100 lb

Percent 

Energy 

Use 

Reduction

Manufacturer Manufacturer

Production Design Options Successively Added

Cost Selling Price 

6.14 0.0% $898 $1,122 Baseline

5.72 6.8% $919 $1,148 Increase compressor EER from 4.59 to 5.2

5.31 13.5% $977 $1,221 Increase Condenser Length by 59%

5.10 16.9% $1,007 $1,259 Change to Batch Water Fill

5.01 18.5% $1,042 $1,303 Switch to ECM Pump Motor

4.82 21.5% $1,261 $1,576 Drain Water Heat Exchanger

kWh = kilowatt-hours; EER = energy efficiency ratio; PSC = permanent split capacitor; ECM = electronically commutated 

motor

Representative Harvest Capacity: 300 lb/24 hours

5.10.3 Design Option Calibrations

DOE calibrated the analysis of each ice maker configuration by comparing the energy use 

predictions associated with given sets of design options, as shown in section 5.10.2, with energy 

usage and design data collected from existing ice maker models. These comparisons are shown 

in Table 5.10.26 through Table 5.10.43. Each table represents the results of the analysis of a 

given ice maker configuration and shows major characteristics of the ice maker configuration at 

the maximum efficiency level in the analysis (excluding ECM and DWHX design options), in 

comparison with one or more units representing the maximum available efficiency level of 

equipment currently available on the market. 

In some cases, the set of design options chosen to represent the maximum efficiency level 

matched the designs of the maximum available efficiency level equipment. In other cases, the 

designs did not match exactly, and the design of the DOE analysis may have had more 

improvement in one component, while the maximum available ice maker had more improvement 

in another component. In order to ensure that DOE was not underestimating the costs associated 

with the overall design improvements required to attain these highest available efficiency levels, 

5-42
DOE estimated the cost differential between the calculated and actual designs by evaluating the 

cost of changing the major components of the analyzed design configuration to match those of 

the maximum available equipment. Major components considered in this estimate were the 

compressor, evaporator, condenser, and condenser fan. In cases where the analyzed maximum 

available design configuration and the maximum available unit had different harvest capacity 

rates, DOE calculated the differential cost for the evaporator and condenser on the basis of 

surface area per capacity, i.e. for a unit that has the same capacity as the analyzed unit with the 

same surface area per capacity as the actual maximum available unit. 

Table 5.10.25 summarizes the calibrations for all of the directly analyzed equipment 

classes, while Table 5.10.26 through Table 5.10.43 show details of the the calculations for each 

class. Some components, namely pump motors for batch units and evaporators for continuous 

units, were not considered in the differential calculations, as the analysis for the maximum 

available efficiency level did not include any design options involving these components. A 

negative cost differential indicates that the DOE analysis predicted a higher cost at the maximum 

available efficiency level compared with the maximum available unit. The computed cost 

differentials are zero or negative in all cases but one, showing that the DOE analysis does not 

underestimate the cost of reaching these higher efficiency levels. For the 22-inch IMH-A-SmallB ice maker, this differential cost is +$4, but the efficiency level of the max available unit si 5% 

higher than the level projected by the DOE analysis. In cases where design details were provided 

confidentially and not available from reverse engineering, the design details for the maximum 

available units are not provided. 

5-43
Table 5.10.25 Maximum Available Calibration Summary and Cost Differentials

Equipment Class

Representative 

Capacity 

lb ice/24h

DOE Analysis 

Maximum

Available

Efficiency Level 

% Below Baseline

Maximum 

Available 

Efficiency Level 

% Below Baseline

Cost Differential 

Moving from 

Analyzed to 

Maximum 

Available 

$

IMH-W-Small-B 300 19.2 19.2 -29

IMH-W-Small-B

(22 inch wide) 300 16.9 16.9 -34

IMH-W-Med-B 850 10.4 14.3 -240

IMH-W-Large-B-2 2600 2.5 2.5 0

IMH-A-Small-B 300 19.3 19.3 -27

IMH-A-Small-B

(22 inch wide) 300 11.6 16.6 +4

IMH-A-Large-B-1 800 16.1 16.1 -74

IMH-A-Large-B-1 

(22 inch wide) 590 5.5 5.5 -13

IMH-A-Large-B-2 1500 6.2 6.0 -130

RCU-NRC-Large-B-1 1500 15.7 15.7 -62

RCU-NRC-Large-B-2 2400 14.9 14.9 -329

SCU-A-Small-B 110 26.6 24.9 -61

SCU-A-Large-B 200 23.5 26.4 -28

SCU-W-Large-B 300 27.6 27.6 0

IMH-A-Small-C 310 19.8 28.0 -30

IMH-A-Large-C 820 17.0 35.7 -11

SCU-A-Small-C 220 21.8 30.1 -62

RCU-NRC-Small-C 610 17.9 18.4 -40

5-44
Table 5.10.26 Comparison of Analyzed and Max Available units for the IMH-W-Small-B 

Equipment Class 

Unit

Analyzed 

Configuration at Max 

Available Efficiency

Max Available

Cost 

Differential

$

Harvest Rate 300 360

Difference from 

Standard (%) -19.2 -19.2

Compressor

Capacity 

(Btu/h) 3,746 4,250

EER (Btu/Wh) 5.2 5.1 -3

Evaporator

Total Plate 

Area (in2) 123 176 +38

Plate Area per 

Capacity (in2-24 

hours/lb ice)

0.41 0.49

Condenser

Pipe Length (in) 124 75

-64 Pipe Outside 

Diameter (in) 0.95 0.72

Length per 

Capacity (in-24 

hours/lb ice)

0.41 0.21

Pump Motor

Type SP SP

Output Power 

(W) 6 6

Total Cost Differential -29

Table 5.10.27 Comparison of Analyzed and Max Available units for the IMH-W-Small-B 

Equipment Class (22 inch wide)

Unit

Analyzed 

Configuration at 

Max Available

Efficiency

Cost 

Differential* 

$

Harvest Rate 300

Difference from 

Standard (%) -16.9

Compressor

Capacity 

(Btu/h) 3,746

EER (Btu/Wh) 5.2 -17

Evaporator Total Plate 

Area (in2) 117 +10

Condenser

Pipe Length (in) 116

Pipe Outside -27

Diameter (in) 0.95

Pump Motor

Type SP

Output Power 

(W) 6

Total Cost Differential -34

*Maximum Available unit data not provided because it was provided under non-disclosure 

agreement.

5-45
Table 5.10.28 Comparison of Analyzed and Max Available units for the IMH-W-Med-B 

Equipment Class

Unit

Analyzed 

Configuration at 

Max Available

Efficiency

Max Available

Cost 

Differential 

$

Harvest Rate 850 832

Difference from 

Standard (%) -10.4 -14.3

Compressor Capacity (Btu/h) 12,000 10,100

EER (Btu/Wh) 6.2 6.1 -4

Evaporator

Total Plate Area 

(in2) 304 304 +10

Plate Area per 

Capacity (in2-24 

hours/lb ice)

0.36 0.37

Condenser

Pipe Length (in) 217 79

Pipe Outside -246

Diameter (in) 1.31 0.92

Length per 

Capacity (in-24 

hours/lb ice)

0.255 0.095

Pump Motor

Type SP SP

Output Power 

(W) 15 15

Total Cost Differential -240

Table 5.10.29 Comparison of Analyzed and Max Available units for the IMH-W-Large-B2 Equipment Class

Unit

Analyzed 

Configuration 

at Max 

Available

Efficiency

Max Available Cost Differential 

$

Harvest Rate 2600 2620

Difference from 

Standard (%) -2.5 -2.5

Compressor

Capacity 

(Btu/h) 34473 34473

EER (Btu/Wh) 6.32 6.32 0

Evaporator Total Plate 

Area (in2) 1156 1156 0

Condenser

Pipe Length (in) Same Same

Pipe Outside 0

Diameter (in) Same Same

Pump Motor

Type Same Same

Output Power 0

(W) Same Same

Total Cost Differential 0

5-46
Table 5.10.30 Comparison of Analyzed and Max Available units for the IMH-A-Small-B 

Equipment Class 

Unit

Analyzed 

Configuration 

at Max 

Available

Efficiency

Max 

Available*

Cost 

Differential*

$

Max Available 2

Harvest Rate 300 300 316

Difference 

from Standard 

(%)

-19.3 -19.3 -15.7

Compressor

Capacity 

(Btu/h) 3,944 ** 4,250

EER (Btu/Wh) 5.2 ** 5.1

Evaporator Total Plate 

Area (in2) 180 164 -25 180

Condenser

Total Face 

Area (in2) 186 192 -9 192

Tube Rows 3 2 3

Length per 

Capacity (in24 hours/lb ice)

1.86 1.28 1.82

Fan Motor

Type PSC PSC

+7

PSC

Output Power 

(W) 19 36 50

Pump Motor

Type SP SP SP

Output Power 

(W) 6 6 6

Cabinet W x D x H (in) 30 x 24.5 x 

21.5

30 x 24.5 x 

21.5 30 x 23.5 x 21.5

Total Cost Differential -27

* Calibration and cost differential based on first Max Available unit.

** Not a R-404A compressor. DOE does not know the performance characteristics of this comparison at the AHRI 540 medium 

temperature rating conditions used for evaluating other compressors for the analysis. For the purposes of the calibration, DOE 

assumed that there would be no cost difference as compared with the compressor used in the analysis. 

5-47
Table 5.10.31 Comparison of Analyzed and Max Available units for the IMH-A-Small-B 

Equipment Class (22 inch wide)

Unit

Analyzed 

Configuration 

at Max 

Available

Efficiency

Max Available

Cost 

Differential

$

Harvest Rate 300 370

Difference from 

Standard (%) -11.6 -16.6

Compressor

Capacity 

(Btu/h) 3,944 3,750

EER (Btu/Wh) 5.2 4.69 -18

Evaporator

Total Plate 

Area (in2) 140 209 +47

Plate Area per 

Capacity (in2-24 

hours/lb ice)

0.47 0.56

Condenser

Total Face Area 

(in2) 182 226 -10

Tube Rows 3 2

Area x Tube 

Rows per 

Capacity (in2 -

24 hours/lb ice)

1.82 1.22

Fan Motor

Type PSC SP

Output Power -15

(W) 19 16

Pump Motor

Type SP SP

Output Power 

(W) 6 6

Total Cost Differential +4

*

5-48
Table 5.10.32 Comparison of Analyzed and Max Available units for the IMH-A-Large-B-1 

Equipment Class 

Unit

Analyzed 

Configuration at 

Max Available

Efficiency

Max Available

Cost 

Differential

$

Harvest Rate 800 844

Difference from 

Standard (%) -16.1 -16.1

Compressor

Capacity 

(Btu/h) 12,000 12,000

EER (Btu/Wh) 6.7 6.2 -17

Evaporator

Total Plate 

Area (in2) 304 304 -25

Plate Area per 

Capacity (in2-24 

hours/lb ice)

0.38 0.36

Condenser

Total Face Area 

(in2) 702 414 -12

Tube Rows 2 3

Area x Tube 

Rows per 

Capacity (in2 -

24 hours/lb ice)

1.76 1.47

Fan Motor

Type PSC SP

Output Power -20

(W) 77.1 70

Pump Motor

Type SP SP

Output Power 

(W) 15 15

Total Cost Differential -74

5-49
Table 5.10.33 Comparison of Analyzed and Max Available units for the IMH-A-Large-B-1 

Equipment Class (22 inch wide)

Unit

Analyzed 

Configuration 

at Max 

Available

Efficiency

Max Available

Cost Differential 

$

Harvest Rate 590 590

Difference from 

Standard (%) -5.5 -5.5

Compressor

Capacity 

(Btu/h) 8,850 8,850

EER (Btu/Wh) 6 5.78 -13

Evaporator Total Plate 

Area (in2) 302 302 0

Condenser

Total Face Area 

(in2) 189 189 0

Tube Rows 2 2

Fan Motor

Type SP SP

Output Power 0

(W) 32 32

Pump Motor

Type SP SP

Output Power 

(W) 15 15

Total Cost Differential -13

Table 5.10.34 Comparison of Analyzed and Max Available units for the IMH-A-Large-B-2 

Equipment Class

Unit

Analyzed 

Configuration at 

Max Available

Efficiency

Cost Differential* 

$

Harvest Rate 1500

Difference from 

Standard (%) -6.2

Compressor

Capacity 

(Btu/h) 23,476

EER (Btu/Wh) 7.16 -29

Evaporator Total Plate 

Area (in2) 617 -31

Condenser

Total Face Area 

(in2) 775 -45

Tube Rows 3

Fan Motor

Type PSC

Output Power -25

(W) 156

Pump Motor

Type SP

Output Power 

(W) 6

Total Cost Differential -130

*Maximum Available unit data not provided because it was provided under non-disclosure 

agreement.

5-50
Table 5.10.35 Comparison of Analyzed and Max Available units for the RCU-NRC-LargeB-1 Equipment Class

Unit

Analyzed 

Configuration at 

Max Available

Efficiency

Max Available

Cost 

Differential

$

Harvest Rate 1,500 1,645

Difference from 

Standard (%) -15.7 -15.7

Compressor

Capacity 

(Btu/h) 23,476 20,000

EER (Btu/Wh) 7.16 6.4 -28

Evaporator

Total Plate 

Area (in2) 617 603 -107

Plate Area per 

Capacity (in2-24 

hours/lb ice)

0.41 0.37

Condenser

Total Face Area 

(in2) 924 1,536 +77

Tube Rows 3 3

Area x Tube 

Rows per 

Capacity (in2 -

24 hours/lb ice)

1.85 2.80

Fan Motor

Type PSC PSC

Output Power -4

(W) 238 186

Pump Motor

Type SP SP

Output Power 

(W) 6 25

Total Cost Differential -62

5-51
Table 5.10.36 Comparison of Analyzed and Max Available units for the RCU-NRC-LargeB-2 Equipment Class

Unit

Analyzed 

Configuration at 

Max Available

Efficiency

Max Available

Cost 

Differential

$

Harvest Rate 2,400 2,256

Difference from 

Standard (%) -14.9 -14.9

Compressor

Capacity 

(Btu/h) 34,473 26,800

EER (Btu/Wh) 6.32 6.2 -8

Evaporator

Total Plate 

Area (in2) 1156 918 -287

Plate Area per 

Capacity (in2-24 

hours/lb ice)

0.48 0.41

Condenser

Total Face Area 

(in2) 1,034 856 *

Tube Rows 2 1

Fan Motor

Type PSC PSC

Output Power -4

(W) 292 186

Pump Motor

Type SP SP

Output Power 

(W) 12 50

Total Cost Differential -329

* The maximum available unit used for calibration utilized a different condenser type than the designed unit. DOE 

has not assigned a cost differential for this component, but expects the cost differential to be negative.

5-52
Table 5.10.37 Comparison of Analyzed and Max Available units for the SCU-A-Small-B 

Equipment Class

Unit

Analyzed 

Configuration at 

Max Available

Efficiency

Max Available

Cost 

Differential

$

Harvest Rate 110 143

Difference from 

Standard (%) -26.6 -24.9

Compressor

Capacity 

(Btu/h) 1,530 1,530

EER (Btu/Wh) 4.6 4.3 -7

Evaporator

Total Plate 

Area (in2) 112 112 -41

Plate Area per 

Capacity (in2-24 

hours/lb ice)

1.02 0.78

Condenser

Total Face Area 

(in2) 116 116 -9

Tube Rows 3 2

Area x Tube 

Rows per 

Capacity (in2 -

24 hours/lb ice)

3.16 1.62

Fan Motor

Type PSC SP

Output Power -4

(W) 6 16

Pump Motor

Type SP SP

Output Power 

(W) 6 6

Total Cost Differential -61

5-53
Table 5.10.38 Comparison of Analyzed and Max Available units for the SCU-A-Large-B 

Equipment Class

Unit

Analyzed 

Configuration at 

Max Available

Efficiency

Max 

Available

Adjusted 

Max 

Available*

Cost 

Differential

$

Harvest Rate (lb 

ice/24h) 200 247 247

Difference from 

Standard (%) -23.5 -26.4 -23.5

Compressor Capacity (Btu/h) 3,746 3,656 3,656

EER (Btu/Wh) 5.2 5.03 5.03 -5

Evaporator

Total Plate Area 

(in2) 111* 150 137 0

Plate Area per 

Capacity (in2-24 

hours/lb ice)

0.55 0.61 0.55

Condenser

Total Area (in2) 192 170 170 -9 Tube Rows 3 3 3

Area x Tube Rows 

per Capacity (in2 -

24 hours/lb ice)

2.88 2.06 2.06

Fan Motor Type PSC SP (two fans) SP (two fans) -14 Output Power (W) 19 5 each ** 5 each **

Pump Motor Type SP PMSM PMSM

Output Power (W) 6 21 21

W x D x H 30 x 30 x 32.875 30 x 29.75 x 

32.75

30 x 29.75 x 

32.75

Total Cost Differential -28

PMSM: Permanent Magnet Synchronous Motor

*Adjusted for energy use equivalent to the maximum available efficiency level of the analysis. Reduction in evaporator surface 

area 8.7% based on the IMH-A-Small-B analysis, for which energy use reduction was 12.8% for a 51% increase in evaporator 

surface area.

**Differential cost calculated as if the max available used one fan with an SP motor with 10W shaft power.*Cost differential 

was based on a 12% larger evaporator to account for the 2.9% in efficiency between the analyzed and actual max available 

units.

5-54
Table 5.10.39 Comparison of Analyzed and Max Available units for the SCU-W-Large-B 

Equipment Class

Unit

Analyzed 

Configuration 

at Max 

Available

Efficiency

Max Available Cost Differential

$

Harvest Rate 300 285

Difference from 

Standard (%) -27.6 -27.6

Compressor

Capacity 

(Btu/h) 3,746 3,746

EER (Btu/Wh) 5.2 4.68 0

Evaporator Total Plate 

Area (in2) 114 114 0

Condenser

Pipe Length (in) 79 79

Pipe Outside 0

Diameter (in) 0.915 0.915

Pump Motor

Type SP SP

Output Power 

(W) 7 7

Total Cost Differential 0

Table 5.10.40 Comparison of Analyzed and Max Available units for the IMH-A-Small-C 

Equipment Class

Unit

Analyzed 

Configuration at 

Max Available

Efficiency

Max Available Cost Differential

$

Harvest Rate 310 531

Ice Hardness 

Factor 73.2 80.8

Difference from 

Standard (%) -19.8 -28

Compressor

Capacity 

(Btu/h) 4,250 4,250

EER (Btu/Wh) 5.4 5.1 -10

Evaporator

Outside 

Diameter (in) 1.75 2.625

Height (in) 6.5 14.875

Condenser

Total Face Area 

(in2) 168 281 -10

Tube Rows 4 3

Area x Tube 

Rows per 

Capacity (in2 -

24 hours/lb ice)

2.17 1.59

Fan Motor

Type PSC PSC

Output Power -10

(W) 70 50

Auger Motor

Type PSC PSC

Output Power 

(W) 124 200

Total Cost Differential -30

5-55
Table 5.10.41 Comparison of Analyzed and Max Available units for the IMH-A-Large-C 

Equipment Class

Unit

Analyzed 

Configuration at 

Max Available

Efficiency

Max 

Available

Max 

Available 

2

Max 

Available 

3

Cost 

Differential

*

$

Harvest Rate 820 874 1100 845

Ice Hardness 

Factor 86 70 58 72

Difference from 

Standard (%) -17.0 -13.2 -16 -35.7

Compressor

Capacity 

(Btu/h) 9,350 5,442 14,100 6,950

EER (Btu/Wh) 6 3.58 6.2 5.4 -21

Evaporator

Outside 

Diameter (in) 4.25 3.5 3 3.25

Height (in) 10.5 19.75 15 12

Condenser

Total Face 

Area (in2) 208 168 341 420 +21

Tube Rows 4 5 3 3

Area x Tube 

Rows per 

Capacity (in2 -

24 hours/lb ice)

1.01 0.96 0.93 1.49

Fan Motor

Type PSC PSC SP SP

Output Power -11

(W) 34 45 60 9

Auger 

Motor

Type PSC PSC PSC PSC

Output Power 

(W) 373 186 186 186

Total Cost Differential -11

* Calibration and cost differential based on Max Available 3

5-56
Table 5.10.42 Comparison of Analyzed and Max Available units for the SCU-A-Small-C 

Equipment Class

Unit

Analyzed 

Configuration at 

Max Available

Efficiency

Max Available

Cost 

Differential

$

Harvest Rate 220 280

Ice Hardness 

Factor 57 85

Difference from 

Standard (%) -21.8 -30.1

Compressor

Capacity 

(Btu/h) 2,053 3,746

EER (Btu/Wh) 5.5 4.68 -27

Evaporator

Outside 

Diameter (in) 1.75 3

Height (in) 10.125 7.5

Condenser

Total Face Area 

(in2) 157 156 -14

Tube Rows 4 3

Area x Tube 

Rows per 

Capacity (in2 -

24 hours/lb ice)

2.85 1.67

Fan Motor

Type PSC SP -20

Output Power 

(W) 20 9

Auger Motor

Type PSC PSC

Output Power 

(W) 75 124

Total Cost Differential -62

5-57
Table 5.10.43 Comparison of Analyzed and Max Available units for the RCU-A-Small-C 

Equipment Class

Unit

Analyzed 

Configuration at 

Max Available

Efficiency

Max Available Cost Differential

$

Harvest Rate 800 835

Ice Hardness 

Factor 57 74

Difference from 

Standard (%) -17.9 -18.4

Compressor

Capacity 

(Btu/h) 5,200 8,850

EER (Btu/Wh) 5.4 5.78 +12

Evaporator

Outside 

Diameter (in) 3 3

Height (in) 12 15

Condenser

Total Face Area 

(in2) 1081 1070 -16

Tube Rows 1 1

Area x Tube 

Rows per 

Capacity (in2 -

24 hours/lb ice)

1.35 1.28

Fan Motor

Type PSC PSC

Output Power -36

(W) 249 124

Auger Motor

Type PSC PSC

Output Power 

(W) 186 186

Total Cost Differential -40

5-58
REFERENCES

1. American Metals Market. (Last accessed November 2008.) <www.amm.com>

2. U.S. Department of Labor, Bureau of Labor Statistics. Producer Price Indices. (Last 

accessed November 2008.) <www.bls.gov/ppi>

3. U.S. Department of Commerce, Bureau Economic Analysis. Gross Domestic Product 

Implicit Price Deflator. (Last accessed November 2008.) 

<https://bea.gov/bea/dn/nipaweb/TableView.asp#Mid>

4. Plastic News. Resin Pricing. (Last accessed March 21, 2008.) 

<http://www.plasticsnews.com/subscriber/headlines.phtml>

5-59
CHAPTER 6. MARKUPS FOR EQUIPMENT PRICE DETERMINATION

TABLE OF CONTENTS

6.1 INTRODUCTION .............................................................................................................. 6-1

6.2 BASELINE, INCREMENTAL, AND OVERALL MARKUPS........................................ 6-2

6.2.1 Baseline Markups................................................................................................. 6-2

6.2.2 Incremental Markups........................................................................................... 6-3

6.2.3 Distribution Channel Market Shares.................................................................... 6-4

6.2.4 Overall Markups .................................................................................................. 6-4

6.3 BASIC ASSUMPTIONS USED TO ESTIMATE WHOLESALER AND 

MECHANICAL CONTRACTOR MARKUPS ................................................................. 6-5

6.4 ESTIMATION OF WHOLESALER MARKUPS ............................................................. 6-6

6.5 ESTIMATION OF MECHANICAL CONTRACTOR MARKUPS.................................. 6-7

6.6 ESTIMATION OF NATIONAL ACCOUNT MARKUPS................................................ 6-9

6.7 SALES TAX ..................................................................................................................... 6-10

6.8 OVERALL MARKUPS.................................................................................................... 6-11

REFERENCES .......................................................................................................................... 6-12

LIST OF TABLES

Table 6.2.1 Distribution Channel Market Shares for Baseline and Higher Efficiency 

Level Automatic Commercial Ice Makers........................................................... 6-4

Table 6.4.1 Typical Wholesaler Expenses and Markups............................................................. 6-7

Table 6.5.1 Typical Mechanical Contractor Expenses and Markups .......................................... 6-8

Table 6.7.1 Combined State and Local Sales Tax Rates ........................................................... 6-10

Table 6.8.1 Baseline Markups by Distribution Channel and Weighted Overall Markup.......... 6-11

Table 6.8.2 Incremental Markups by Distribution Channel and Weighted Overall Markup .... 6-11

LIST OF FIGURES

Figure 6.5.1 Cumulative Distribution of Mechanical Contractor Markups for Automatic 

Commercial Ice-Making Equipment.................................................................... 6-9

6-i
CHAPTER 6. MARKUPS FOR EQUIPMENT PRICE DETERMINATION

6.1 INTRODUCTION

One of the most important inputs to the life-cycle cost (LCC) and payback period (PBP) 

analysis and the national impact analysis is the projected installed cost of automatic commercial 

ice-making (ACIM) equipment. Installed cost includes equipment purchase price and installation 

costs. The U.S. Department of Energy (DOE) projects the equipment purchase price by using 

multipliers called “markups” that are applied to the manufacturer production cost (MPC) to 

estimate the customer purchase price of the equipment. The manufacturer markup, projected as 

part of the engineering analysis (chapter 5 of the final rule technical support document (TSD)), 

converts the MPC into manufacturer selling price (MSP). Distribution channel markups are also 

applied to the MSP based on the distribution channels through which customers currently 

purchase the equipment. 

DOE determined that customers purchase automatic commercial ice makers through three 

major distribution channels:

Manufacturer  Customer (National Account Channel)

Manufacturer  Wholesaler  Customer (Wholesaler Channel)

Manufacturer  Wholesaler  Local Contractor  Customer (Contractor Channel)

The three distribution channels can also be viewed as a single-step (national accounts)

channel, a two-step (wholesaler) channel, and a three-or-more-step (contractor) channel.

DOE calculated an aggregate average markup value for each distribution channel. DOE 

then weight-averaged the three distribution channel markup values using the market share of 

each distribution channel to obtain weighted-average distribution channel markups (see section 

6.2.3 for market share estimates). Based on suggestions from stakeholders at the Framework and 

preliminary analysis public meetings, DOE used the same weighted-average distribution channel 

markup values for all ACIM equipment classes.

DOE calculated a weighted-average sales tax using the population of each state as the 

corresponding weight. DOE then multiplied the weighted-average sales tax by the weightedaverage distribution channel markups to estimate overall distribution channel markup values for 

automatic commercial ice makers.

DOE notes that Framework and preliminary analysis public meeting stakeholders 

indicated mechanical contractors, specifically, are seldom involved in the distribution channel; 

however, local contractors or installers are generally involved. DOE uses mechanical contractor 

cost data in the estimation of markups since it is the best data available for quantifying the cost 

of a local contractor. 

This chapter presents DOE’s methodology for calculating the three distribution channel 

markups, weighted-average sales tax, and overall distribution channel markups.

6-1
6.2 BASELINE, INCREMENTAL, AND OVERALL MARKUPS

Baseline markups are markups that convert the MSP of equipment at the baseline 

efficiency level (efficiency level 1) to customer purchase price. Incremental markups are 

markups that convert the projected increase in MSP of equipment at efficiency level 2 or higher 

to a projected increase in customer purchase price compared to the price of equipment at 

efficiency level 1. Overall markups include the weighted-average values of markups based on the 

estimated market share for each distribution channel and the sales tax. 

6.2.1 Baseline Markups

If the baseline equipment is sold through the National Account Channel, the following 

equation defines the estimated equipment price:

EEEEEENNNNNNNN AAAA BB BBBB = MMMMMMBB BBBB × MMMMNNNNNNNN AAAA BB BBBB × (1 + SSSS)

Eq. 6.1

Where:

EQPNATL ACCNT BASE = national account price to the customer of baseline equipment ($/unit),

MFGBASE = MSP of baseline equipment ($/unit), 

MUNATL ACCT BASE = national account markup on baseline equipment, and

ST = weighted-average sales tax rate.

If the baseline equipment is sold through the Wholesaler Channel:

EEEEEEWWWWWW BB BBBB = MMMMMMBB BBBB × MMMMWWWWWW BB BBBB × (1 + SSSS)

Eq. 6.2

Where:

EQPWHOLE BASE = wholesaler price of baseline equipment ($/unit), and

MUWHOLE BASE = wholesaler markup on baseline equipment.

If the baseline equipment is sold to a customer through the Contractor Channel:

EEEEEE BB BBBB = MMMMMMBB BBBB × MMMMWWWWWW BB BBBB × MMMMMM BB BBBB × (1 + SSSS)

Eq. 6.3

Where:

EQP CONT BASE = contractor or installer price of baseline equipment ($/unit), and

MUMECH CONT BASE = mechanical contractor markup on baseline equipment.

6-2
6.2.2 Incremental Markups

Incremental markups are cost multipliers that relate increments in the MSP of equipment 

at higher efficiency levels to the corresponding increments in customer purchase price. DOE 

projects the increment in MSP of equipment at higher efficiency levels by subtracting the MSP 

of equipment at the baseline efficiency level (MFGBASE) from the projected MSP of equipment at 

higher efficiency levels:

MMMMMMII II = MMMMMM HH EEEE LL −MMMMMMBB BBBB

Eq. 6.4

Where:

MFG INCR = projected increment in MSP of higher efficiency equipment ($/unit), and

MFGHIGHER EFF LEVEL = projected MSP of equipment at higher efficiency level ($/unit).

If the equipment is sold through the National Account Channel, DOE projects the 

increase in equipment price using the following equation:

EEEEEENNNNNNNN AAAA II II = MMMMMMII II × MMMMNNNNNNNN AAAA II II × (1 + SSSS)

Eq. 6.5

Where:

EQPNATL ACCNT INCR = projected increment in customer purchase price to the national account

customer ($/unit), and

MUNATL ACCT INCR = national account incremental markup.

If the customer acquires the higher efficiency equipment through the Wholesaler or 

Contractor Channels:

EEEEEEWWWWWW II II = MMMMMMII II × MMMMWWWWWW II II × (1 + SSSS)

Eq. 6.6

Where:

EQPWHOLE INCR = projected incremental wholesaler price ($/unit), and

MUWHOLE INCR = incremental wholesaler markup.

EEEEEE II II = MMMMMMII II × MMMMWWWWWW II II × MMMMMM II II × (1 + SSSS)

Eq. 6.7

Where:

EQPCONT INCR = projected incremental contractor or installer price ($/unit), and

MUMECH CONT INCR = incremental mechanical contractor markup.

6-3
6.2.3 Distribution Channel Market Shares

Table 6.2.1 provides the estimated market shares for the three distribution channels for 

automatic commercial ice makers used for the final rule analysis of this rulemaking. DOE used 

these distribution channel market shares to estimate overall markups. DOE obtained the market 

shares from U.S. Census Bureau data for the commercial refrigeration equipment sector, of 

which automatic commercial ice makers comprise one of the component product lines. The U.S. 

Census Bureau data allocated a small portion of the market to the National Account Channel. At 

the preliminary analysis public meeting, stakeholders indicated that the National Account 

Channel does not truly exist in the ACIM market because customers rely on third parties to 

install and service machines. For the final rule analysis, the market share previously allocated to 

National Account Channel was combined with the Wholesaler Channel. 

Based on stakeholder input at the Framework and preliminary analysis public meetings, 

DOE modeled the majority of automatic commercial ice makers as purchased through a threestep distribution channel. DOE’s estimates are listed in Table 6.2.1.

Table 6.2.1 Distribution Channel Market Shares for Baseline and Higher Efficiency Level 

Automatic Commercial Ice Makers

National Account Channel Wholesaler Channel Contractor Channel

Automatic Commercial Ice 

Makers, All 0% 38% 62%

Source: U.S. Census Bureau, Sector 42: EC0742SXSB01: Wholesale Trade: Subject Series - Misc Subjects: Sales by Class of 

Customer for the United States: 2007, data for NAICS code 423740, Refrigeration equipment and supplies merchant wholesalers. 

Downloaded March 4, 2011.

6.2.4 Overall Markups

DOE estimated overall markup values separately for both baseline and incremental 

markups by combining the weighted-average sales tax with the weighted-average distribution 

channel markups. DOE estimated overall baseline markup values using this equation:

EEEEEE CC BB BBBB = MMMMMMBB BBBB

× (WW NNNNNNNN AAAA BB BBBB × MMMMNNNNNNNN AAAA BB BBBB + WW WWWWWW BB BBBB × MMMMWWWWWW BB BBBB

+ WW BB BBBB × MMMMWWWWWW BB BBBB × MMMMMM BB BBBB)

× (1 + SSSS)

= MMMMMMBB × MMMM BB BBBB

Eq. 6.8

Where:

EQPCUST BASE = customer purchase price for baseline equipment ($/unit),

WTNATL ACCNT BASE = estimated market share of baseline equipment sold through the National 

Account Channel (%),

6-4
WTWHOLE BASE = estimated market share of baseline equipment sold through the Wholesaler 

Channel (%),

WTCONT BASE = estimated market share of baseline equipment sold through the Contractor Channel 

(%), and

MUOVERALL BASE = estimated overall baseline markup.

DOE projected overall incremental markups using this equation:

EEEEEE CC II II = MMMMMMII II

× (WW NNNNNNNN AAAA II II × MMMMNNNNNNNN AAAA II II + WW WWWWWW II II × MMMMWWWWWW II II

+ WW II II × MMMMWWWWWW II II × MMMMMM II II) × (1 + SSSS)

 = MMMMMMII II × MMMM II II

Eq. 6.9

Where:

EQPCUST INCR = projected increment in customer purchase price of equipment at a higher 

efficiency level compared to baseline equipment ($/unit), 

WTNATL ACCNT INCR = projected market share of higher efficiency level equipment sales through the 

National Account Channel (%),

WTWHOLE INCR = projected market share of higher efficiency level equipment sales through the 

Wholesaler Channel (%), 

WTCONT INCR = projected market share of higher efficiency level equipment sales through the 

Contractor Channel (%), and

MUOVERALL INCR = overall incremental markup.

EEEEEE CC = EEEEEE CC BB BBBB + EEEEEE CC II II

Eq. 6.10

DOE used the overall baseline and overall incremental markups to estimate the baseline 

customer price (EQPCUST BASE) and to project the incremental customer price (EQPCUST INCR). For a 

particular piece of equipment at a higher efficiency level, the projected total price of that 

equipment to the customer (EQPCUST) is the sum of the baseline customer price (EQPCUST BASE) and 

the projected incremental customer price (EQPCUST INCR).

6.3 BASIC ASSUMPTIONS USED TO ESTIMATE WHOLESALER AND 

MECHANICAL CONTRACTOR MARKUPS

DOE based the wholesaler markups on industry balance sheet data and based the 

mechanical contractor markups on U.S. Census Bureau data for the plumbing, heating, and airconditioning (PHAC) industry.

1 DOE obtained the wholesale industry balance sheet data from 

Heating, Air-conditioning & Refrigeration Distributors International (HARDI), the trade 

association representing wholesalers of refrigeration and heating, ventilating, and airconditioning (HVACR) equipment.

2 DOE compiled the U.S. Census Bureau PHAC data 

following the same format as used for the balance sheet data for wholesalers. These balance 

6-5
sheets itemize the costs incurred by firms that supply and install PHAC equipment. DOE 

estimated the wholesaler and mechanical contractor markups based on three key assumptions 

about ACIM costs:

1) The balance sheets accurately represent the various average costs incurred by firms 

distributing and installing automatic commercial ice makers.

2) The wholesaler and contractor costs can be divided between (a) costs that vary in 

proportion to the MSP (variable costs) and (b) costs that do not vary with MSP (fixed 

costs).

3) Wholesaler and contractor prices vary in proportion to the wholesaler and contractor 

costs included in the balance sheets.

The basis for the first assumption is that the industry balance sheets itemize costs

incurred by a firm by a number of cost categories, including direct costs to purchase or install the 

equipment, operating labor and occupancy costs, and other operating costs and profit. Although 

wholesalers and contractors tend to handle multiple equipment lines (including refrigerators, 

freezers, beverage dispensers, and other related equipment), the data provide the most credible

available information for ACIM distribution costs.

The basis for the second assumption is that firms must incur some costs regardless of the 

cost of the goods being sold—costs such as sales, accounting and management staff, office 

expenses, depreciation, and many other operating costs do not vary with the MSP. For purposes 

of this analysis, DOE refers to these costs as fixed. Other costs, such as uncollectable accounts, 

bad debts, and profit margins, vary as a function of the total cost of sales, including the cost of 

goods sold. For purposes of this analysis, DOE refers to these costs as variable.

The basis for the third assumption is that the HVACR wholesaler and contractor industry 

is competitive, while customer demand for ACIM equipment is assumed to be inelastic (i.e., the 

demand is not expected to decrease significantly with an increase in price of equipment). The 

large number of HVACR firms listed in the 2007 economic census indicates the competitive 

nature of the market. For example, the 2007 census lists more than 700 HVACR manufacturers,3

1,300 wholesalers of refrigeration equipment,4 and 91,000 HVAC contractors.

5 Following 

standard economic theory, competitive firms facing inelastic demand either set prices in line with 

costs or quickly go out of business.6

6.4 ESTIMATION OF WHOLESALER MARKUPS

Annually, HARDI conducts a confidential survey of its member firms in which 

wholesalers report median data. For what they describe as a typical distributor, HARDI

disaggregates revenues into categories, including direct cost of goods sold (equipment (MSP), 

materials, fuels, and contracted services), labor costs, occupancy costs, other operating costs, and 

profit. Appendix 6A presents the data for a typical HARDI distributor in terms of specific types 

of expenses within these categories. Table 6.4.1 summarizes these expenses in units of expenses

per dollar of sales revenue and revenue per dollar of goods sold. As shown in the first column of

Table 6.4.1, the direct cost of equipment sold represents $0.739 per dollar of sales revenue. In 

other words, for every $1.00 the typical wholesaler takes in as sales revenue, it pays $0.739 in

direct costs. Labor expenses account for $0.151 per dollar of sales revenue, occupancy expenses 

6-6
account for $0.035, other operating expenses account for $0.052, and profit accounts for $0.023

per dollar sales revenue.

Table 6.4.1 Typical Wholesaler Expenses and Markups

Description

Wholesale Firm Expenses or Revenue

Per Dollar

Sales Revenue

Per Dollar Cost 

of Goods Sold

Direct Cost of Equipment Sales: Cost of goods sold $0.739 $1.000

Labor Expenses: Salaries and benefits $0.151 $0.204

Occupancy Expenses: Rent, maintenance, and utilities $0.035 $0.047

Other Operating Expenses: Depreciation, advertising, and insurance $0.052 $0.070

Profit $0.023 $0.031

Baseline Markup (MUWHOLE BASE): Revenue per dollar cost of goods sold * 1.353

Incremental Markup (MUWHOLE INCR): Increased revenue per dollar increase cost of goods sold* 1.101

Source: Heating, Air-conditioning & Refrigeration Distributors International. 2013. 2013 Profit Report (2012 Data).

* Numbers are rounded and may not add up to the totals.

The last column of Table 6.4.1 shows the data converted from costs per dollar revenue 

into revenue per dollar cost of goods sold. DOE performed this conversion by dividing each cost 

category in the first data column of Table 6.4.1 by $0.739 (i.e., equipment expenditure per dollar 

revenue). The data in this column show that, for every $1.00 the wholesaler spends on equipment 

costs, the wholesaler earns $1.00 in sales revenue to cover the equipment cost, $0.204 to cover 

labor costs, $0.047 to cover occupancy expenses, $0.070 for other operating expenses, and 

$0.031 in profits. This totals to $1.353 in sales revenue earned for every $1.00 spent on 

equipment costs. Therefore, the wholesaler baseline markup (MUWHOLE BASE) is 1.353 ($1.353 ÷ 

$1.00).

DOE also used the data in the last column of Table 6.4.1 to estimate incremental 

markups. The total incremental markup depends on which of the costs in Table 6.4.1 are variable 

and which are fixed. For example, for a $1.00 increase in the manufacturer equipment price, if all 

of the other costs scale with the MSP (i.e., all costs are variable), the increase in wholesaler price 

will be $1.353, implying that the incremental markup is 1.353, or the same as the baseline

markup. However, if none of the other costs is variable, then a $1.00 increase in the MSP will 

lead to a $1.00 increase in the wholesaler price, for an incremental markup of 1.0. DOE assumed

that the labor and occupancy costs will be fixed and that the other operating costs and profit will 

scale with the MSP (i.e., be variable). In this case, for a $1.00 increase in the MSP, the 

wholesaler price will increase by $1.101. Therefore, the wholesaler incremental markup 

(MUWHOLE INCR) is 1.101 ($1.101 ÷ $1.00). See appendix 6A for additional details and data used 

for markup calculations.

6.5 ESTIMATION OF MECHANICAL CONTRACTOR MARKUPS

DOE derived markups for mechanical contractors from U.S. Census Bureau data for

PHAC contractors. This sector includes establishments primarily engaged in installing and 

servicing PHAC equipment, which may include installations, additions, alterations, maintenance, 

and repairs. The U.S. Census Bureau data for the PHAC sector include detailed statistics for 

establishments with payrolls, similar to the data reported by HARDI for wholesalers. The 

primary difference is that the U.S. Census Bureau reports itemized revenues and expenses for the 

PHAC industry as a whole in total dollars, rather than in typical values for the average or 

6-7
representative business. Because of this, DOE assumed that the total dollar values that the 

U.S. Census Bureau reported, once converted to percentages, represent revenues and expenses 

for the average or typical contracting business. Table 6.5.1 summarizes the typical expenses for 

mechanical contractors. The first data column of Table 6.5.1 lists expenses per dollar sales 

revenue (appendix 6A contains the full set of data). The direct cost of sales represents about 

$0.678 per dollar sales revenue to the mechanical contractor. Labor expenses account for $0.175

per dollar sales revenue, occupancy expenses account for $0.022 per dollar sales revenue, other 

operating expenses account for $0.039, and profit makes up $0.086 per dollar sales revenue.

Table 6.5.1 Typical Mechanical Contractor Expenses and Markups

Description

Mechanical Contractor Firm Expenses or 

Revenue

Per Dollar

Sales Revenue

Per Dollar Cost 

of Goods Sold

Direct Cost of Equipment Sales: Cost of goods sold $0.678 $1.000

Labor Expenses: Salaries (indirect) and benefits $0.175 $0.258

Occupancy Expense: Rent, maintenance, and utilities $0.022 $0.032

Other Operating Expenses: Depreciation, advertising, and insurance $0.039 $0.058

Net Profit Before Taxes $0.086 $0.127

Baseline Markup (MUMECH CONT BASE): Revenue per dollar cost of goods sold * 1.474

Incremental Markup (MUMECH CONT INCR): Increased revenue per dollar increase cost of goods sold 1.184

Sources: U.S. Census Bureau. 2007. Plumbing, Heating, and Air-Conditioning Contractors. Sector 23: 238220. Construction: 

Geographic Area Series: Detailed Statistics for Establishments: 2007; DOE estimates.

* Numbers are rounded and presented so the totals match. The actual markups in the LCC analysis are 1.474 and 1.184.

DOE converted these expenses per dollar sales into revenue per dollar cost of goods sold 

by dividing each figure in the first data column by $0.678. The data in the last column of 

Table 6.5.1 show that, for every $1.00 the mechanical contractor spends on equipment costs, the 

typical mechanical contractor earns $1.00 in sales revenue to cover the equipment cost, $0.258 to 

cover labor costs, $0.032 to cover occupancy expenses, $0.058 for other operating expenses, and 

$0.127 in profits. This totals $1.474 in sales revenue earned for every $1.00 spent on equipment 

costs. This results in a mechanical contractor baseline markup (MUMECH CONT BASE) of 1.474

($1.474÷ $1.00).

DOE was also able to use the data in the last column in Table 6.5.1 to estimate the 

incremental markups by separating the fixed and variable costs. For example, if all of the other 

costs scale with the equipment price (i.e., all costs are variable), the increase in mechanical

contractor price will be $1.474, an incremental markup of 1.474, or the same as the baseline 

markup. However, if none of the other costs are variable, then a $1.00 increase in the equipment 

price will lead to a $1.00 increase in the mechanical contractor price, for an incremental markup 

of 1.0. DOE assumed the labor and occupancy costs are fixed and the other operating costs and 

profit scale with the equipment price (i.e., are variable). In this case, for a $1.00 increase in the 

equipment price, the typical mechanical contractor price will increase by $1.184, giving a 

mechanical contractor incremental markup (MUMECH CONT INCR) of 1.184 ($1.184 ÷ $1.00).

Mechanical contractor costs differ in various regions of the country depending on

availability of labor, cost of living, and union versus non-union workforce. Because many 

mechanical contractor costs differ systematically by state, DOE characterized the markups 

developed from U.S. Census Bureau data with a probability distribution based on a state-by-state

6-8
analysis of U.S. Census Bureau data for PHAC contractors. The state-by-state analysis provided 

a distribution on the relative markups of mechanical contractors in the United States.

Figure 6.5.1 shows the cumulative probability distribution of the state markup index that 

DOE used to characterize the mechanical contractor baseline and incremental markups. As 

mentioned in section 6.1, the Contractor Channel markup index was varied by state. The baseline 

and incremental markups in Table 6.5.1 are the national average markup values for the 

Contractor Channel. In the LCC analysis (TSD chapter 8), these national average values were 

multiplied by the state markup index to obtain the Contractor Channel markups for a particular 

state. DOE used the state-level markup data in combination with state-level electricity and water 

prices in the LCC and PBP analysis to provide additional granularity in the analysis of whether 

customers are better or worse off at the efficiency levels analyzed in this rulemaking. 

Figure 6.5.1 Cumulative Distribution of Mechanical Contractor Markups for Automatic 

Commercial Ice-Making Equipment

6.6 ESTIMATION OF NATIONAL ACCOUNT MARKUPS

National accounts are generally large accounts—large enough to circumvent the 

Wholesaler Channel and negotiate directly with, and purchase directly from, manufacturers to 

achieve significantly lower equipment prices. The manufacturers, in turn, must cover additional 

expenses related to the distribution of the equipment, but gain in terms of negotiating agreedupon sales volumes with each national account.

To estimate the price savings realized from equipment purchased through the national

account distribution chain, DOE assumed that the resulting equipment price increase is one half 

of that realized from distribution through the Wholesaler Channel. In other words, if the price 

markup resulting from the wholesaler markups is $100, the national account markup is $50.

Because DOE does not have data suggesting typical values for the actual national account 

markup, it chose a value halfway between the MSP and the wholesaler markup.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9

Cumulative Percentages (%)

Markup

Mechanical Contractor Markup by State

6-9
For example, using a baseline MSP of $1,000 for an automatic commercial ice maker

delivered to a restaurant, and a baseline wholesaler markup of 1.353, the resulting baseline 

customer equipment price for sales through a wholesaler (without sales taxes) is $1,353 ($1,000

× 1.353). The dollar value increase due to the above distribution channel markups is $353

($1,353 − $1,000). Under the assumption that national account customers realize equipment 

price increases equal to one-half of that through the wholesaler distribution channel, the dollar 

value of the estimated equipment price increase under the national account is $176.50. The 

resulting equipment price is $1,176.50 ($176.50 + $1,000), which results in a national account 

baseline markup of 1.177 ($1,177 ÷ $1,000). Using a value of 1.101 for the wholesaler 

incremental markup and a similar calculation results in a total national account incremental 

markup of 1.051 ($1,050.50 ÷ $1,000).

6.7 SALES TAX

Sales tax includes state and local sales taxes that are applied to the customer price of 

automatic commercial ice makers. The sales tax is a multiplicative factor that increases the 

customer equipment price by an amount equal to MSP plus markups multiplied by the sales tax 

rate. DOE derived sales tax rates from the Sales Tax Clearinghouse.

7 Table 6.7.1 lists tax rates 

by state. DOE applied the state-level combined tax rate to the estimated value of state-level 

ACIM shipments to obtain the total purchase cost to a customer located in a given state.

The distribution of sales tax rates range from zero to 9.45 percent, with a mean value of 

6.25 percent. DOE applied sales taxes to the customer equipment price irrespective of the 

distribution channel or the state in which the customer is located. For the national impact 

analysis and other national-level analyses, DOE calculated a weighted-average, national-level 

sales tax rate by multiplying the relative population of each state by the tax rates listed in

Table 6.7.1. The weighted-average, national-level sales tax rate is 7.16 percent. 

Table 6.7.1 Combined State and Local Sales Tax Rates

State Sales Tax State Sales Tax State Sales Tax

Alabama 8.55% Kentucky 6.00% North Dakota 5.95%

Alaska 1.30% Louisiana 8.75% Ohio 7.10%

Arizona 7.15% Maine 5.50% Oklahoma 8.35%

Arkansas 8.90% Maryland 6.00% Oregon 0.00%

California 8.45% Massachusetts 6.25% Pennsylvania 6.40%

Colorado 6.10% Michigan 6.00% Rhode Island 7.00%

Connecticut 6.35% Minnesota 7.20% South Carolina 7.15%

Delaware 0.00% Mississippi 7.05% South Dakota 5.40%

Dist. of Col. 5.75% Missouri 7.50% Tennessee 9.45%

Florida 6.65% Montana 0.00% Texas 7.95%

Georgia 7.10% Nebraska 6.00% Utah 6.70%

Hawaii 4.40% Nevada 7.85% Vermont 6.05%

Idaho 6.05% New Hampshire 0.00% Virginia 5.60%

Illinois 8.05% New Jersey 6.95% Washington 8.90%

Indiana 7.00% New Mexico 6.60% West Virginia 6.10%

Iowa 6.85% New York 8.40% Wisconsin 5.45%

Kansas 7.85% North Carolina 6.90% Wyoming 5.50%

Source: The Sale Tax Clearinghouse, www.thestc.com/STrates.stm. Last accessed June 17, 2014

6-10
6.8 OVERALL MARKUPS

Table 6.8.1 presents the baseline markup values for each distribution channel and the 

overall baseline markup values used for automatic commercial ice makers. Table 6.8.2 presents

the incremental markup values for each distribution channel and the overall incremental markup 

values. Note that the markups shown for wholesaler/contractor represent the product of the 

wholesaler chain and contractor chain markups (i.e., they are the product of two terms shown 

separately in the equations in section 6.2).

Table 6.8.1 Baseline Markups by Distribution Channel and Weighted Overall Markup

Wholesaler Wholesaler/

Contractor

National Account

(Mfg Direct) Overall Markup

Channel Mult 1.353 1.995 1.177 1.751

Sales Tax 1.072 1.072 1.072 1.072

Overall Multiplier* 1.450 2.138 1.261 1.876

*Overall multiplier taken from the LCC model and not subject to rounding of inputs.

Table 6.8.2 Incremental Markups by Distribution Channel and Weighted Overall Markup 

Wholesaler Wholesaler/

Contractor

National Account

(Mfg Direct) Overall Markup

Channel Mult 1.101 1.305 1.051 1.227

Sales Tax 1.072 1.072 1.072 1.072

Overall Multiplier* 1.180 1.398 1.126 1.315

*Overall multiplier taken from the LCC model and not subject to rounding of inputs.

DOE used the overall markups to estimate the customer price of baseline and higher

efficiency equipment. For example, if the MSP of a baseline self-contained unit is $1,000, DOE 

obtained the customer purchase price for this baseline equipment by multiplying the MSP by the 

overall markup value of 1.876, or $1,876.00. If the incremental MSP of the equipment at a 

higher efficiency level is $100, then DOE obtained the incremental customer purchase price of 

this equipment by multiplying the MSP by the overall incremental markup of 1.315 to obtain an 

incremental customer purchase price of $131.50. The customer purchase price of this higher

efficiency equipment is the sum of the baseline price and the increment, or $2,007.50 ($1,876 + 

$131.50).

6-11
REFERENCES

1. U.S. Census Bureau. 2007 Economic Census, Release Date: 11/24/2009. Sector 23: 

EC0723A1: Construction: Geographic Area Series: Detailed Statistics for Establishments: 

2007. (Last accessed October 29, 2013.) 

<http://factfinder2.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=ECN_

2007_US_23A1&prodType=table>

2. Heating, Air-conditioning & Refrigeration Distributors International. 2013 Profit Report 

(2012 Data). 2013. Columbus, OH.

3. U.S. Census Bureau. 2007 Economic Census, Release Date: 10/30/2009, Sector 31: 

EC0731I1: Manufacturing: Industry Series: Detailed Statistics by Industry for the United 

States: 2007. Air-Conditioning, Refrigeration, and Forced Air Heating Manufacturing. 

(Last accessed November 12, 2014.) 

<http://factfinder2.census.gov/faces/nav/jsf/pages/searchresults.xhtml?refresh=t#none>

4. U.S. Census Bureau. 2007 Economic Census, Release Date: 11/24/2009, Sector 42: 

EC0742I1: Wholesale Trade: Industry Series: Preliminary Summary Statistics for the 

United States: 2007. HVAC equip. merchant wholesalers. (Last accessed October 29, 

2011.) 

<http://factfinder2.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=ECN_

EC0742I1_FTP&prodType=document>

5. U.S. Census Bureau. 2007 Economic Census, Release Date: 8/14/2009, Sector 23: 

EC0723I1: Construction: Industry Series, Preliminary Detailed Statistics for 

Establishments: 2007. Plumbing, heating, and air-conditioning contractors. (Last accessed 

October 29, 2013.) 

<http://factfinder2.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=ECN_

2007_US_23I1&prodType=table>

6. Pindyck, R. and D. Rubinfeld. Microeconomics. s.l. 5th ed. 2000. Prentice Hall: Upper 

Saddle River, NJ.

7. The Sale Tax Clearinghouse. State sales tax rates along with average combined city and 

county rates. (Last accessed June 17, 2014.) <http://www.thestc.com/STrates.stm>

6-12
APPENDIX 6A. DATA FOR EQUIPMENT PRICE MARKUPS

TABLE OF CONTENTS

6A.1 INTRODUCTION ........................................................................................................ 6A-1

6A.2 DETAILED WHOLESALER COST DATA ............................................................... 6A-1

6A.3 DETAILED MECHANICAL CONTRACTOR DATA............................................... 6A-2

6A.4 ESTIMATION OF WHOLESALER AND MECHANICAL CONTRACTOR 

MARKUP STANDARD DEVIATIONS ..................................................................... 6A-3

LIST OF TABLES

Table 6A.2.1 Disaggregated Costs and Expenses for Wholesalers.......................................... 6A-1

Table 6A.3.1 Mechanical Contractor Expenses and Markups Used to Scale the Incremental 

Markups ............................................................................................................ 6A-2

Table 6A.4.1 Mechanical Contractor Baseline Markups by State, 2007.................................. 6A-3

6A-i
APPENDIX 6A. DATA FOR EQUIPMENT PRICE MARKUPS

6A.1 INTRODUCTION 

This appendix provides further details on information presented in chapter 6, Markups for 

Equipment Price Determination.

6A.2 DETAILED WHOLESALER COST DATA

Chapter 6 presents wholesaler revenues and costs in aggregated form, based on the

Heating, Air-conditioning & Refrigeration Distributors International (HARDI) 2013 Profit 

Report (2012 Data). Table 6A.2.1 provides the complete breakdown of costs and expenses from

the 2013 Profit Report (2012 Data). The column labeled “Scaling” indicates which expenses 

were assumed to scale with only the baseline markup and which were assumed to scale with both 

the baseline and the incremental markups. As described in chapter 6, only those expenses that 

scale with baseline and incremental costs are marked up when there is an incremental change in 

equipment costs.

Table 6A.2.1 Disaggregated Costs and Expenses for Wholesalers*

Item Percent of Revenue Scaling

Cost of Goods Sold 73.9

Gross Margin 26.1

Payroll Expenses 15.1

Baseline

Executive Salaries & Bonuses 1.6

Branch Manager Salaries and Bonuses 1.3

Sales Executive Salaries & Commissions 0.5

Outside Sales Salaries & Commissions 2.3

Inside/Counter Sales/Wages 2.6

Purchasing Salaries/Wages 0.5

Credit Salaries/Wages 0.2

IT Salaries/Wages 0.2

Warehouse Salaries/Wages 1.4

Accounting Salaries/Wages 0.5

Delivery Salaries/Wages 0.8

All Other Salaries/Wages & Bonuses 0.8

Payroll Taxes 1.0

Group Insurance 1.0

Benefit Plans 0.4

Occupancy Expenses 3.5

Baseline

Utilities: Heat, Light, Power, Water 0.4

Telephone 0.3

Building Repairs & Maintenance 0.3

Rent or Ownership in Real Estate 2.5

Other Operating Expenses 5.2

Baseline & Incremental

Sales Expense (incl. advertising and promotion) 0.9

Insurance (business liability & casualty) 0.2

Depreciation 0.4

Vehicle Expenses 1.2

Personal Property Taxes/Licenses 0.1

Collection Expense (collection, credit card fees) 0.3

Bad Debt Losses 0.2

6A-1
Table 6A.2.1 (cont)

Item Percent of Revenue Scaling

Data Processing 0.3

Employee Training 0.1

All Other Operating Expenses 1.5

Total Operating Expenses 23.8

Operating Profit 2.3

Baseline & Incremental Other Income (0.4)

Interest Expense 0.4

Other Non-operating Expenses 0.0

Profit Before Taxes 2.3

Source: Heating, Air-conditioning & Refrigeration Distributors International. 2013 Profit Report (2012 Data). 2013. Columbus, 

OH.

*Wholesaler costs and expenses are percentage values as opposed to the per dollar of sales revenue values shown in chapter 6.

6A.3 DETAILED MECHANICAL CONTRACTOR DATA

Chapter 6 presents mechanical contractor revenues and costs in aggregated form, based 

on U.S. Census Bureau data. Table 6A.3.1 shows the complete breakdown of costs and expenses 

provided by the U.S. Census Bureau. The column labeled “Scaling” indicates which expenses 

were assumed to scale with only the baseline markup and which were assumed to scale with both 

the baseline and incremental markups. As described in chapter 6, only those expenses that scale 

with baseline and incremental costs are marked up when there is an incremental change in 

equipment costs.

Table 6A.3.1 Mechanical Contractor Expenses and Markups Used to Scale the Incremental 

Markups*

Item Dollar Value Percentage Scaling

Total Cost of Equipment Sales $107,144,428 67.80%

Baseline

Cost of materials, components, and supplies $59,023,964 37.35%

Payroll, construction workers $31,373,558 19.85%

Cost of construction work subcontracted out to others 13,646,192 8.64%

Cost of selected power, fuels, and lubricants $3,100,714 1.96%

Gross Margin $50,895,129 32.20% -

Payroll Expenses $27,626,376 17.48%

Fringe benefits, all employees $13,585,040 8.60% Baseline

Payroll, other employees $14,041,336 8.89%

Occupancy Expenses Baseline

Rental cost for machinery, equipment, and buildings; 

Cost of repairs to machinery and equipment; 

Purchased communication services

$3,436,208 2.17%

Baseline

Other Operating Expenses $6,165,776 3.90% Baseline & Incremental

Depreciation charges during year $2,297,550 1.50%

Computers; Insurance and other business services; 

Advertising and promotions; Taxes and license fees

$3,868,226 2.40%

Net Profit Before Income Taxes $13,666,769 8.60% Baseline & Incremental

Source: U.S. Census Bureau. 2007 Economic Census, Release Date: 8/14/2009, Sector 23: EC0723I1: Construction: Industry 

Series: Preliminary Detailed Statistics for Establishments: 2007. (Last accessed October 29, 2013.)

<http://factfinder2.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=ECN_2007_US_23I1&prodType=table>

*Mechanical contractor costs and expenses are first presented as total dollar values and then converted to percentage values. 

This is in contrast to per dollar of sales revenue values shown in chapter 6.

6A-2
6A.4 ESTIMATION OF WHOLESALER AND MECHANICAL CONTRACTOR 

MARKUP STANDARD DEVIATIONS

The U.S. Department of Energy (DOE) used the U.S. Census Bureau’s Economic Census 

data to estimate mechanical contractor markup distributions. For mechanical contractors, the 

2007 census data included state-level plumbing, heating, and air-conditioning data for total value 

of work, number of firms, cost of goods sold, cost of subcontract work, cost of materials, and 

construction payroll, as shown in Table 6A.4.1.

DOE used the state-by-state variation in heating, ventilating, and air-conditioning 

(HVAC) contractor markups as a component of state-by-state variation in the life-cycle cost 

(LCC) studies. In the case of the contractor markups, the variation in contractor markup by state 

is captured explicitly in the subsequent LCC analysis. By “selecting” a state during the Monte 

Carlo analysis, DOE varied the installation costs widely. Looking at contractor markups on a 

relative basis, the lowest state is 90 percent of the average markup while the highest state is 

119 percent of the average. Using population as a weighting factor, HVAC was combined with 

other factors that vary by state to create one of the key sets of cost components varied during the 

analysis.

Table 6A.4.1 Mechanical Contractor Baseline Markups by State, 2007*

State

Value of 

Construction 

$000

Cost of 

Subcontract 

Work 

$000

Cost of 

Materials 

$000

Construction 

Payroll

$000

Baseline 

Markup

Alabama 2,010,305 113,782 876,341 411,100 1.435

Alaska 583,171 52,245 171,575 120,909 1.692

Arizona 3,522,116 179,103 1,508,903 638,469 1.514

Arkansas 1,065,754 68,417 461,924 213,054 1.434

California 16,726,969 1,070,065 6,330,469 3,464,667 1.539

Colorado 3,056,988 261,734 1,195,057 627,663 1.467

Connecticut 1,704,668 145,740 628,720 361,411 1.501

Delaware 481,900 D D 163,343 1.421

District of Columbia 34,600 D D 50,439 1.458

Florida 9,061,426 783,859 3,736,811 1,733,721 1.449

Georgia 4,700,799 365,010 2,224,110 740,722 1.412

Hawaii 800,221 46,753 270,723 137,646 1.758

Idaho 900,698 62,252 387,181 167,732 1.459

Illinois 7,641,642 602,251 2,833,489 1,622,307 1.511

Indiana 4,002,323 431,558 1,319,523 854,157 1.536

Iowa 1,868,483 144,869 801,239 359,775 1.431

Kansas 1,395,359 106,307 580,764 279,636 1.443

Kentucky 1,747,925 128,902 674,500 353,958 1.510

Louisiana 1,997,044 162,063 776,784 378,582 1.516

Maine 580,816 46,692 234,993 113,162 1.471

Maryland 5,329,135 698,381 2,009,957 1,031,222 1.425

Massachusetts 4,099,301 488,098 1,475,525 817,754 1.474

Michigan 4,420,638 604,850 1,569,113 841,985 1.466

Minnesota 3,402,921 386,669 1,230,126 698,535 1.470

6A-3
Table 6A.4.1 (cont)

State

Value of 

Construction 

$000

Cost of 

Subcontract 

Work 

$000

Cost of 

Materials 

$000

Construction 

Payroll 

$000

Baseline 

Markup

Mississippi 1,025,452 76,709 449,851 189,011 1.433

Missouri 3,335,124 345,285 1,319,142 689,171 1.417

Montana 483,578 42,988 216,792 85,678 1.400

Nebraska 1,004,296 94,170 455,264 205,904 1.330

Nevada 2,327,842 121,091 988,605 490,859 1.454

New Hampshire 620,761 39,784 D 128,512 1.472

New Jersey 5,062,336 496,174 1,825,407 1,015,432 1.517

New Mexico 891,914 67,987 356,961 170,711 1.497

New York 10,364,779 1,219,468 3,568,182 1,972,687 1.533

North Carolina 5,111,396 341,129 2,288,841 1,001,832 1.407

North Dakota 360,683 36,037 148,617 70,403 1.414

Ohio 5,618,591 568,837 2,115,568 1,125,401 1.475

Oklahoma 1,352,943 94,153 581,079 249,032 1.464

Oregon 1,893,678 124,070 701,468 412,418 1.530

Pennsylvania 6,487,476 579,901 2,628,602 1,370,864 1.417

Rhode Island 631,202 56,234 229,692 124,727 1.537

South Carolina 1,991,303 126,123 847,690 352,877 1.501

South Dakota 386,186 11,037 158,375 69,605 1.616

Tennessee 2,595,613 189,293 1,159,952 484,997 1.415

Texas 10,810,308 823,920 4,605,624 2,102,520 1.435

Utah 1,746,398 146,052 769,140 319,812 1.414

Vermont 294,806 21,021 D 63,015 1.472

Virginia 4,623,151 347,055 1,791,740 960,534 1.492

Washington 4,111,543 370,741 1,546,819 816,533 1.504

West Virginia 655,100 D D 90,660 1.464

Wisconsin 2,926,545 234,962 1,125,779 662,893 1.446

Wyoming 289,391 14,391 128,922 54,792 1.461

Average Baseline Markup 1.477

Standard Deviation 0.070

Relative Standard Deviation 0.048

Source: U.S. Census Bureau. 2007 Economic Census, Release Date: 11/24/2009, Sector 23: EC0723A1: 

Construction: Geographic Area Series: Detailed Statistics for Establishments: 2007. (Last accessed October 29, 

2013.)

<http://factfinder2.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=ECN_2007_US_23I1&prodTyp

e=table>

*The Census Bureau withheld data for some states due to sample sizes and the size of errors relative to means. For 

states where a D appears under the headings for Subcontractor Costs, Materials & Supplies, or Construction Payroll, 

data was withheld. States missing one or more variables were set equal to an average of neighboring states’ baseline 

markup.

6A-4
CHAPTER 7. ENERGY AND WATER USE ANALYSIS

TABLE OF CONTENTS

7.1 INTRODUCTION .............................................................................................................. 7-1

7.2 ANNUAL ENERGY CONSUMPTION............................................................................. 7-1

7.2.1 Equipment Class Energy Use and Harvest Rate Capacity...................................... 7-1

7.2.2 Utilization Factor .................................................................................................... 7-2

7.3 ANNUAL WATER CONSUMPTION............................................................................... 7-3

7.4 WEIGHTING FACTORS FOR LARGE BATCH EQUIPMENT CLASSES................... 7-4

REFERENCES ............................................................................................................................ 7-6

LIST OF TABLES

Table 7.2.1 Distribution Used for LCC Crystal Ball for Utilization Factor ................................ 7-2

Table 7.2.2 Total Annual Energy Usage...................................................................................... 7-3

Table 7.3.1 Total Annual Water Consumption............................................................................ 7-4

Table 7.4.1 Large Batch Equipment Class Weighting Factors.................................................... 7-5

7-i
CHAPTER 7. ENERGY AND WATER USE ANALYSIS

7.1 INTRODUCTION

This chapter presents the U.S. Department of Energy’s (DOE’s) analysis of annual 

energy and water usage at various efficiency levels of automatic commercial ice makers. These 

estimated values of annual energy consumption (AEC) in kilowatt-hours per year (kWh/yr) and 

annual water consumption (AWC) in gallons of water per year are key inputs to the life-cycle 

cost and payback period analysis (chapter 8 of the final rule technical support document (TSD))

and national impacts analysis (chapter 10 of the TSD).

This chapter discusses the methodology used to estimate the annual energy and water 

consumption for representative-sized units for each automatic commercial ice maker equipment 

class.

7.2 ANNUAL ENERGY CONSUMPTION

For the final rule analysis, DOE estimated the AEC for every combination of equipment 

class and efficiency level considered in the analysis. To estimate the AEC, estimated energy use 

per 100 lb of ice developed in the engineering analysis (see TSD chapter 5), assumed equipment 

utilization factors, and maximum daily harvest rates for each combination are used to calculate a 

daily energy consumption value for the ice-making function. In addition to the ice-making 

related energy consumption, estimated daily standby energy usage is also added to yield a total 

energy consumption value for a single 24-hour period. Multiplying the sum of the daily icemaking related energy and standby energy use by 365 results in the total AEC for each specific 

equipment class and efficiency level combination.

For automatic commercial ice makers, the equation for AEC is: 

AECi,j = (Ei,j ÷ 100× Hi× Ui + SEi × (1 – Ui)) × 365

Eq. 7.1

Where: 

AECi,j = calculated annual energy consumption for equipment class i, at efficiency level j

(kWh/year),

Ei,j = estimated energy use for equipment class i, at efficiency level j (kWh/100 lb ice),

Hi = harvest rate for equipment class i (pounds of ice produced per 24 hours),

Ui = assumed utilization for equipment class i (percent of time producing and harvesting ice), and

SEi = estimated “standby” energy use for equipment class i (kWh/24 hours).

7.2.1 Equipment Class Energy Use and Harvest Rate Capacity

For each equipment class, a baseline energy use value is estimated, noted as efficiency 

level 1. Efficiency level 1 is the initial value against which energy saving percentages for higher 

efficiency levels are estimated. A more complete breakdown of how the incremental percentage 

savings values are derived is described in TSD chapter 5. Harvest rate capacity, which provides a 

representative value for the pounds of ice that can be produced for each ice-making equipment

7-1
class, does not vary by efficiency level within an equipment class. The values used as estimates

for energy use (Ei,j) and harvest rate (Hi) are taken from the engineering analysis presented in 

chapter 5 and are designed to be representative of a given equipment class.

7.2.2 Utilization Factor

Ice-making equipment is not assumed to be making ice 24 hours out of every day. 

Instead, a utilization factor (Ui) is used as the estimate for the percent of time the ice maker is 

actively producing ice. In the final rule analysis, this value is estimated at 42 percent across all 

equipment classes and efficiency levels. The utilization factor was based on data provided by 

manufacturers and data obtained from a report found via an Internet search.

1 The total data set of 

metered utilization factors included 80 observations. The average utilization factor for the 88 

observations was 42 percent. DOE used this factor for the estimation of electricity and water 

usage values.

In the life-cycle cost (LCC) model, DOE used Crystal Ball to allow the utilization factor 

to vary. The 88 observations were divided into bins based on 0 to 10 percent, 10 to 20 percent, 

etc. For each bin, the utilization factor was the average for the observations within the bin. The 

distribution is shown on Table 7.2.1.

Table 7.2.1 Distribution Used for LCC Crystal Ball for Utilization Factor

Bin 

Upper 

Level

%

Weight 

Assigned to 

Bin

%*

Average 

Utilization 

Factor for 

Observations 

in Bin 

%

10 0 0

20 2.3 16

30 31.8 26

40 26.1 35

50 12.5 45

60 10.2 54

70 6.8 63

80 5.7 76

90 3.4 86

100 1.1 91

*Numbers might not add to 100 percent due to 

rounding.

The complement to the amount of time the ice-making equipment is actively producing is 

the time that the ice-making equipment is not actively producing or harvesting ice. Energy used 

during non-producing periods is referred to as standby use energy. Test data indicates that 

standby use represents an extremely small level of usage. As a conservative assumption, DOE 

used a value of 5 watts (W) for all equipment classes but believes the average value may actually

be lower. The standby loss assumption translates to energy consumption of 0.07 kWh per day.

Calculated annual energy values for each equipment class and available efficiency levels 

are provided on Table 7.2.2. For each equipment class listed, AEC values for the maximum 

7-2
available energy efficiency levels are included in the column with the highest numbered 

efficiency level for that equipment class.

Table 7.2.2 Total Annual Energy Usage

Equipment Class*

Annual Electricity Usage**

kWh

Level 

1

Level 

2

Level 

3

Level 

3A

Level 

4

Level 

4A

Level 

5

Level 

6

Level 

7

IMH-W-Small-B 2,832 2,551 2,411 2,270 2,227 2,162 

IMH-W-Med-B 6,040 5,439 5,138 4,951 

IMH-W-Large-B† 10,750 9,891 

 IMH-W-Large-B1 9,166 8,405 

 IMH-W-Large-B2 15,868 14,693 

IMH-A-Small-B 3,535 3,184 3,009 2,901 2,833 2,658 2,640 

IMH-A-Large-B† 8,078 7,272 6,931 6,881 6,622 6,411 

 IMH-A-Large-B1 7,350 6,617 6,251 6,192 5,885 5,636 

 IMH-A-Large-B2 11,999 10,802 10,591 

RCU-Large-B† 12,118 10,908 10,316 10,066 

 RCU-Large-B1 11,679 10,514 9,931 9,664 

 RCU-Large-B2 18,671 16,807 16,077 

SCU-W-Large-B 3,499 3,151 2,978 2,804 2,630 2,464 

SCU-A-Small-B 2,177 1,962 1,854 1,747 1,639 1,532 1,473 

SCU-A-Large-B 3,011 2,713 2,563 2,414 2,265 2,141 

IMH-A-Small-C 4,299 3,872 3,658 3,445 3,231 3,201 

IMH-A-Large-C 8,270 7,445 7,033 6,621 6,348 

RCU-Small-C 7,737 6,966 6,580 6,195 5,809 5,688 

SCU-A-Small-C 3,416 3,077 2,907 2,738 2,568 2,515 

* See chapter 5 for a description of these abbreviations.

** A blank cell indicates the efficiency level is not defined.

† IMH-W-Large-B, IMH-A-Large-B, and RCU-Large-B values are weighted averages of the two typical units. Weights are 

shown on Table 7.4.1.

7.3 ANNUAL WATER CONSUMPTION

DOE estimated the AWC for ice-making equipment similarly to the way it estimated the 

AEC. To calculate the AWC, first the estimated potable water use and condenser water use per 

100 lb of ice are summed to provide a total water use per 100 lb of ice. Factoring in the total 

water use per 100 lb of ice produced with the assumed equipment utilization factor and 

representative maximum daily harvest rates yields a total daily water use figure for each 

equipment class. Multiplying the daily use values by 365 days in a year provides the total AWC. 

For automatic commercial ice makers, the equation for AEC is: 

AWCij = (PWij + CWij) ÷ 100× Hi × Ui × 365

Eq. 7.2

Where: 

AWCij = calculated total annual water consumption for equipment class i, at efficiency level j 

(gal/yr),

PWij = estimated potable water use for equipment class i, at efficiency level (gal/100 lb ice),

7-3
CWij = estimated condenser water use for equipment class i, at efficiency level (gal/100 lb ice),

Ui = assumed utilization for equipment class i (percent of time producing and harvesting ice),

and

Hi = harvest rate for equipment class i (pounds of ice produced per 24 hours).

Estimates of the annual combined potable water and condenser water usage are 

summarized in Table 7.3.1. Note that the increase in usage at level 4 for IMH-A-Small-B and 

IMH-A-Large-B1 occurs because of the merging of 22-inch and non-22-inch cost-efficiency 

curves for those two equipment classes (see TSD chapters 5 and 8), and not because design 

options were studied that increased potable water usage.

Table 7.3.1 Total Annual Water Consumption

Equipment Class*

Annual Water Usage**

1,000 gal/yr

Level 

1

Level 

2

Level 

3

Level 

3A

Level 

4

Level 

4A

Level 

5

Level 

6

Level 

7

IMH-W-Small-B 80.7 79.7 78.9 78.9 78.9 78.9 

IMH-W-Med-B 223.6 223.6 223.6 223.6 

IMH-W-Large-B† 502.9 502.9 

 IMH-W-Large-B1 421.1 421.1 

 IMH-W-Large-B2 767.2 767.2 

IMH-A-Small-B 11.3 11.3 11.3 11.3 11.7 11.7 11.7 

IMH-A-Large-B† 30.2 30.2 27.6 27.6 27.7 27.7 

 IMH-A-Large-B1 27.4 27.4 24.3 24.3 24.4 24.4 

 IMH-A-Large-B2 45.7 45.7 45.7 

RCU-Large-B† 47.4 47.4 47.4 47.4 

 RCU-Large-B1 45.7 45.7 45.7 45.7 

 RCU-Large-B2 72.0 72.0 72.0 

SCU-W-Large-B 74.0 69.4 69.4 69.4 69.4 69.4 

SCU-A-Small-B 5.7 5.7 5.7 5.7 3.4 3.4 3.4 

SCU-A-Large-B 9.1 9.1 9.1 6.1 6.1 6.1 

IMH-A-Small-C 5.7 5.7 5.7 5.7 5.7 5.7 

IMH-A-Large-C 17.0 17.0 17.0 17.0 17.0 

RCU-Small-C 14.6 14.6 14.6 14.6 14.6 14.6 

SCU-A-Small-C 4.0 4.0 4.0 4.0 4.0 4.0 

* See chapter 5 for a description of these abbreviations.

** A blank cell indicates the efficiency level is not defined.

† IMH-W-Large-B, IMH-A-Large-B, and RCU-Large-B values are weighted averages of the two typical units. Weights are 

shown on Table 7.4.1.

7.4 WEIGHTING FACTORS FOR LARGE BATCH EQUIPMENT CLASSES

Because the harvest rate categories are so wide for large equipment classes, in three cases 

(IMH-W-Large-B, IMH-A-Large-B, and RCU-Large-B) the analyses in this final rule focused on 

two typical units for large batch equipment classes. For illustrative purposes, on input and 

summary results tables a class level value was created to give an idea of the impact at the class 

level. The class level value is strictly illustrative as all life-cycle cost and national impact 

analyses took place at the typical unit level. For some tables, such as the national energy savings 

or net present value results, the typical units were added together to create the class values. In 

other tables, a weighted average was created. The weighted averages were based on weighting 

7-4
factors calculated from the relative shipments of automatic commercial ice makers in each size 

category. The weights are shown on Table 7.4.1.

Table 7.4.1 Large Batch Equipment Class Weighting Factors

Equipment Class and

Typical Unit

Weight 

%

IMH-W-Large-B

 IMH-W-Large-B1 76.4

 IMH-W-Large-B2 23.6

IMH-A-Large-B

 IMH-A-Large-B1 84.3

 IMH-A-Large-B2 15.7

RCU-Large-B

 RCU-Large-B1 93.7

 RCU-Large-B2 6.3

7-5
REFERENCES

1. Karas, A. and D. Fisher. A Field Study to Characterize Water and Energy Use of 

Commercial Ice-Cube Machines and Quantify Saving Potential. 2007. Fisher-Nickel, Inc.: 

San Ramon, CA.

7-6
CHAPTER 8. LIFE-CYCLE COST AND PAYBACK PERIOD ANALYSIS

TABLE OF CONTENTS

8.1 INTRODUCTION .............................................................................................................. 8-1

8.1.1 General Approach for Life-Cycle Cost and Payback Period Analysis................... 8-1

8.1.2 Overview of Life-Cycle Cost and Payback Period Inputs...................................... 8-3

8.2 LIFE-CYCLE COST INPUTS ........................................................................................... 8-7

8.2.1 Definition ................................................................................................................ 8-7

8.2.2 Total Installed-Cost Inputs...................................................................................... 8-8

8.2.2.1 Baseline Energy Consumption and Manufacturer Selling Price.............. 8-9

8.2.2.2 Candidate Standard Level Energy Consumption and Manufacturer Selling 

Price Increases ...................................................................................................... 8-11

8.2.2.3 Overall Markup...................................................................................... 8-13

8.2.2.4 Installation Cost ..................................................................................... 8-13

8.2.2.5 Equipment Price Trends......................................................................... 8-16

8.2.2.6 Weighted Average Total Installed Cost................................................. 8-17

8.2.3 Operating-Cost Inputs........................................................................................... 8-20

8.2.3.1 Electricity Price Analysis....................................................................... 8-20

8.2.3.2 Electricity Price Trend ........................................................................... 8-22

8.2.3.3 Water Cost ............................................................................................. 8-23

8.2.3.4 Water Price Trend.................................................................................. 8-24

8.2.3.5 Repair Cost............................................................................................. 8-24

8.2.3.6 Maintenance Cost................................................................................... 8-26

8.2.3.7 Lifetime.................................................................................................. 8-27

8.2.3.8 Discount Rate......................................................................................... 8-27

8.2.3.9 Compliance Date of Standard ................................................................ 8-30

8.3 PAYBACK PERIOD INPUTS......................................................................................... 8-31

8.3.1 Definition .............................................................................................................. 8-31

8.3.2 Inputs..................................................................................................................... 8-31

8.4 LIFE-CYCLE COST AND PAYBACK PERIOD RESULTS......................................... 8-31

8.4.1 Life-Cycle Cost Results........................................................................................ 8-32

8.4.2 Payback Period Results......................................................................................... 8-35

8.4.3 Rebuttable Presumption Payback Period.............................................................. 8-37

8.5 DETAILED RESULTS .................................................................................................... 8-38

REFERENCES .......................................................................................................................... 8-40

LIST OF TABLES

Table 8.1.1 Summary Information of Inputs for the Determination of Life-Cycle Cost and 

Payback Period ....................................................................................................... 8-7

Table 8.2.1 Inputs for Total Installed Costs................................................................................. 8-8

Table 8.2.2 Equipment Classes Evaluated for the Automatic Commercial Ice-Making Equipment 

Standard Life-Cycle Cost and Payback Period Analysis...................................... 8-10

8-i
Table 8.2.3 Baseline Energy Consumption Levels and Estimated MSP Values for the Primary 

Equipment Classes................................................................................................ 8-11

Table 8.2.4 Baseline and Incremental Manufacturer Selling Prices for the Primary Equipment 

Classes* ................................................................................................................ 8-12

Table 8.2.5 Energy Consumption Values for the Primary Equipment Classes, by Efficiency 

Level* ................................................................................................................... 8-12

Table 8.2.6 Installation Cost Assumptions for 22-Inch Units ................................................... 8-14

Table 8.2.7 Installation Cost Assumptions for Non-22-Inch Units with Size Growth.............. 8-15

Table 8.2.8 Installation Cost by Equipment Class, U.S. Average ............................................. 8-15

Table 8.2.9 Installation Cost Indices (National Value = 100.0) ................................................ 8-16

Table 8.2.10 Costs and Markups for Determination of Weighted Average Total Installed Costs 

(IMH-A-Small-B)................................................................................................. 8-17

Table 8.2.11 Weighted Average Equipment Price, Installation Cost, and Total Installed Costs for 

IMH-A-Small-B at U.S. Average Conditions (2013$)......................................... 8-19

Table 8.2.12 Inputs Used to Determine Operating Costs.......................................................... 8-20

Table 8.2.13 Commercial Electricity Prices by State (2013 cents/kWh) .................................. 8-21

Table 8.2.14 Derived National Average Commercial Electricity Prices and Ratios by Business 

Type ...................................................................................................................... 8-22

Table 8.2.15 Commercial Water Prices by State (2013$/1,000 gallons)................................... 8-24

Table 8.2.16 Repair Cost Assumptions...................................................................................... 8-25

Table 8.2.17 Repair Cost by Equipment Class.......................................................................... 8-25

Table 8.2.18 Annual Maintenance Costs by Equipment Class and Efficiency Level ............... 8-26

Table 8.2.19 Probability of Equipment Failure by Year............................................................ 8-27

Table 8.2.20 Derivation of Typical Discount Rates by Building Type ..................................... 8-30

Table 8.4.1 LCC Savings Distribution Results for Equipment Class IMH-A-Small-B ............ 8-33

Table 8.4.2 Mean LCC Savings for All Equipment Classes and Efficiency Levels ................. 8-34

Table 8.4.3 Median LCC Savings for All Equipment Classes and Efficiency Levels .............. 8-35

Table 8.4.4 Payback Period Distribution Results for IMH-A-Small-B..................................... 8-36

Table 8.4.5 Median Payback Period for All Equipment Classes and Efficiency Levels........... 8-37

Table 8.4.6 Rebuttable Presumption Payback Periods by Efficiency Level and Equipment Class

.............................................................................................................................. 8-38

Table 8.5.1 Summary of Results of LCC and PBP Analysis for IMH-A-Small-B Equipment 

Class...................................................................................................................... 8-39

LIST OF FIGURES

Figure 8.1.1 Flow Diagram of Inputs for the Determination of Life-Cycle Cost and Payback 

Period...................................................................................................................... 8-6

Figure 8.2.1 National Electricity Price Trends for Commercial Rates to 2047......................... 8-23

Figure 8.4.1 LCC and Installed Cost Variation over Efficiency Levels for IMH-A-Small-B 

Equipment Class................................................................................................... 8-32

Figure 8.4.2 LCC Savings Ranges by Efficiency Level for Equipment Class IMH-A-Small-B... 8-

33

Figure 8.4.3 Payback Period for All Efficiency Levels for the Equipment Class IMH-A-Small-B

.............................................................................................................................. 8-36

8-ii
CHAPTER 8. LIFE-CYCLE COST AND PAYBACK PERIOD ANALYSIS

8.1 INTRODUCTION

This chapter describes the analysis the U.S. Department of Energy (DOE) has carried out 

to evaluate the economic impacts of possible energy conservation standards developed for 

automatic commercial ice makers on individual commercial customers, henceforth referred to as 

customers. The effect of standards on customers includes changes in operating costs (usually 

decreased) and changes in purchase costs (usually increased). This chapter describes two metrics 

used to determine the effect of standards on customers:

• Life-cycle cost (LCC). The total customer cost over the life of the equipment is the 

sum of installed cost (purchase and installation cost) and operating costs 

(maintenance, repair, water,

a and energy costs). Future operating costs are discounted 

to the time of purchase, and summed over the lifetime of equipment.

• Payback period (PBP). Payback period is the estimated amount of time it takes 

customers to recover the assumed higher purchase price of more energy efficient 

equipment through lower (undiscounted) operating costs.

An efficiency improvement in automatic commercial ice makers that is financially 

attractive to a customer will typically have a low PBP and a low LCC associated with it. 

The remainder of this section outlines the general approach and provides an overview of 

the inputs to the LCC and PBP analysis of automatic commercial ice makers. Inputs to the LCC 

and PBP analysis are discussed in detail in sections 8.2 and 8.3. Results for the LCC and PBP 

analysis are presented in sections 8.4 and 8.5.

DOE performed the calculations discussed in this chapter using a Microsoft Excel

spreadsheet, which is available at www1.eere.energy.gov/buildings/appliance_standards/

commercial/automatic_ice_making_equipment.html. Appendix 8A includes instructions for 

using the spreadsheet. Appendix 8B presents the detailed results. Appendix 8C discusses 

uncertainty in the LCC modeling process.

8.1.1 General Approach for Life-Cycle Cost and Payback Period Analysis

This section summarizes DOE’s approach to the LCC and PBP analysis for automatic 

commercial ice makers.

As part of the engineering analysis (chapter 5 of the final rule technical support document 

(TSD)), DOE explored various efficiency levels based on increasing efficiency (decreased 

energy consumption) and, typically, increasing manufacturer selling price (MSP) values. For the 

LCC and PBP analysis, DOE chose a maximum of seven levels, referred to herein as efficiency 

levels. DOE treats each higher efficiency level as a potential new standard level. 

a Water costs, as used in this chapter, are the total of water and wastewater costs. Wastewater utilities tend to not 

meter customer wastewater flows, and they base billings on water commodity billings. For this reason, water usage 

is used as the basis for both water and wastewater costs, and the two are aggregated in the LCC and PBP analysis.

8-1

 
The first efficiency level (level 1) in each equipment class is the least efficient and the 

least expensive equipment in that equipment class. For many batch-type equipment classes, level 

1 equipment is equipment that just meets the efficiency standards for cube-type equipment set by 

the Energy Policy Act of 2005 (EPACT 2005). For equipment not currently covered by 

standards, level 1 reflects the least efficient equipment currently available. The higher efficiency 

levels (level 2 and up) have progressive increases in efficiency and equipment cost from level 1.

The highest efficiency level in each equipment class corresponds to the maximum efficiency 

level obtainable with non-proprietary technology and without significantly increasing the 

footprint of the equipment (see final rule TSD chapter 5 for details). 

The installed cost of equipment to a customer is the sum of the equipment purchase price 

and installation cost. The purchase price includes manufacturer production cost (MPC), to which 

a manufacturer markup is applied to obtain the MSP. DOE calculated this value as part of the 

engineering analysis (chapter 5 of the final rule TSD). DOE then applied additional markups to 

the equipment to account for the costs associated with the distribution channels for this type of 

equipment (chapter 6 of the final rule TSD). Installation costs vary by state depending on the 

prevailing labor rates. 

Operating costs for automatic commercial ice makers are the sum of maintenance costs, 

repair costs, energy costs, and water costs. Customers incur these costs over the life of the 

equipment. To facilitate cost comparisons, DOE discounted operating costs to the discount year

(2018, the effective date of the standards established as part of this rulemaking). The sum of the 

installed cost and the operating costs, discounted to reflect the present value, is the LCC.

Generally, customers incur higher installed costs when they purchase higher efficiency 

equipment, and these cost increments will be partially or wholly offset by savings in the 

operating costs over the lifetime of the equipment. Usually, the savings in operating costs are due 

to savings in energy costs because higher efficiency equipment uses less energy over the lifetime 

of the equipment. In some equipment classes the operating cost savings also include savings in 

potable water usage because one energy-saving design option also reduces the amount of water 

used in batch-type machines. The LCC of higher efficiency equipment can be lower compared to 

lower efficiency equipment. DOE calculated the change in LCC for each efficiency level of each 

equipment class.

DOE obtained the PBP of higher efficiency equipment by dividing the increase in the 

installed cost by the decrease in annual operating cost. For this calculation, DOE used the sum of 

the first year operating cost changes as the estimate of the annual decrease in operating cost, 

noting that some of the repair and maintenance costs used herein are annualized estimates of 

costs. DOE calculated a PBP for each efficiency level of each equipment class.

Apart from MSP, installation costs, and maintenance and repair costs, other important 

inputs for the LCC and PBP analysis are distribution chain markups and sales tax, equipment 

energy and water consumption, latest available electricity and water prices and future price 

trends, equipment lifetime, and discount rates.

DOE estimated many inputs for the LCC and PBP analysis from the best available data,

and in some cases DOE used inputs that are generally accepted values within the automatic 

8-2
commercial ice-making (ACIM) industry. In general, there is uncertainty associated with many

inputs, so DOE carried out the LCC and PBP analysis in the form of Monte Carlo simulations 

using ranges of values and probability distributions for certain inputs that account for the 

uncertainties. DOE presents the results of the LCC and PBP analysis in the form of mean and 

median LCC savings; percentages of customers experiencing net savings, net cost, and no impact 

in LCC; and median PBP. For each equipment class, DOE carried out 10,000 Monte Carlo 

simulations using Microsoft Excel and Crystal Ball, a commercially available Excel add-in for 

performing Monte Carlo simulations.

DOE calculated LCC savings and PBP by comparing the installed costs and LCC values 

of a given standards scenario against those of the base-case scenario. The base-case scenario is 

the scenario in which customers purchase equipment in the absence of the proposed energy 

conservation standard. A standards-case scenario is a scenario in which customers purchase 

equipment after the hypothetical energy conservation standard goes into effect. The number of 

standards scenarios for an equipment class is equal to one less than the total number of efficiency 

levels in that equipment class because each efficiency level above efficiency level 1 represents a 

potential new standard. Usually, the market will offer equipment at various efficiencies. 

Therefore, for both the base-case and the standards-case scenarios in the LCC and PBP analysis, 

DOE calculated the market shares of the efficiency levels using a method described in final rule 

TSD chapter 10.

Different types of buildings and industries face different energy prices, and apply

different discount rates to purchase decisions. DOE analyzed variability and uncertainty by 

performing the LCC and PBP calculations for seven types of buildings: health care, lodging,

foodservice, retail, education, food sales, and offices. 

The typical equipment lifetime is approximately 7 to 10 years with an average of 8.5 

years. There were no data to suggest that lifetimes are unique to each equipment class. Therefore, 

DOE used a distribution of equipment lifetimes that is defined by Weibull survival functions, 

with an average value of 8.5 years (see section 8.2.3.7).

Another important factor influencing the LCC and PBP analysis is the geographic

location in which the automatic commercial ice maker is installed. Inputs that vary by location

include installation costs, water and energy prices, distribution chain markups, and sales tax. At 

the national level, DOE explicitly modeled variability in water price, electricity price, and 

markups using probability distributions based on the relative populations in all states. 

Results of the LCC and PBP analysis are presented at the end of this chapter and in 

appendix 8B.

8.1.2 Overview of Life-Cycle Cost and Payback Period Inputs

DOE categorized inputs to the LCC and PBP analysis as: (1) inputs for establishing the 

total installed cost; and (2) inputs for calculating operating costs.

8-3
The primary inputs for establishing the total installed cost are:

• Baseline manufacturer selling price: The price charged by the manufacturer (when 

selling to either a wholesaler or customer) for equipment meeting the baseline

efficiency level. The MSP includes a manufacturer’s markup, which converts the 

MPC to MSP.

• Price trends (learning): A method of adjusting the MSP over time to account for 

increasing cost efficiency in the production of ACIM equipment. DOE assumed that,

with time and experience, the real cost of producing equipment will decrease 

marginally. The development of price trends is discussed in appendix 8D.

• Efficiency level manufacturer selling price increase: The incremental change in MSP

associated with producing equipment at a given higher efficiency level. 

• Markups and sales tax: The distribution chain markups and sales tax used to convert 

the MSP to a customer purchase price. Final rule TSD chapter 6 presents the 

methodology used to determine markups and sales taxes.

• Installation cost: The cost to the customer of installing the equipment, not including 

the equipment cost. DOE assumed the cost of installation as a one-time cost, and it is 

intended to represent the cost of labor, overhead, and other miscellaneous materials 

and parts.

The primary inputs for calculating the operating costs are:

• Equipment energy consumption: The energy consumed by an automatic commercial 

ice maker per year, expressed in kilowatt-hours (kWh). DOE calculated the kWh/100 

lb ice per day as part of the engineering analysis, and the engineering results were 

used to calculate annual energy usage in the energy and water usage analysis (final 

rule TSD chapter 7).

• Equipment water consumption: The amount of condenser water (used to cool 

refrigerant in the open-loop condenser system) and potable water (used to form ice) 

used per year, expressed in gallons. DOE calculated the water usage per 100 lb ice 

per day as part of the engineering analysis, and the engineering results were used to 

calculate annual water consumption in the energy and water usage analysis (final rule 

TSD chapter 7).

• Electricity price: The price per kilowatt-hour, in cents or dollars, paid by each 

customer for electricity. DOE used average commercial electricity prices in each 

state, as determined from the Energy Information Administration (EIA) form 826 data 

for 2013. DOE then adjusted the average commercial prices to reflect the fact that the 

seven types of businesses analyzed pay electricity prices that are different from 

average commercial prices. Section 8.2.3.1 details the development of electricity 

prices and the data sources used.

• Electricity price trends: DOE used the EIA’s Annual Energy Outlook 2014

(AEO2014)

1 to forecast electricity prices. For the results presented in this chapter, 

DOE used the regional prices from the AEO2014 reference case to forecast future 

electricity price

8-4
• Water and wastewater prices: DOE combined the prices for water and wastewater in 

dollars per 1,000 gallons. DOE used price data from the 2012 American Water Works

Association (AWWA) Water and Wastewater Survey, supplemented with data from 

earlier AWWA surveys.

2 No data existed that disaggregated water prices for 

individual building types, so DOE varied prices by state only and not by building type 

within a state. Section 8.2.3.1 details the development of prices and the data sources 

used.

• Water and wastewater price trends: DOE used the Consumer Price Index (CPI) data 

for water related consumption (1974–2013) in developing a real growth rate for water 

and wastewater price forecasts. 

• Maintenance costs: DOE calculated maintenance costs as an annual expense 

representing the estimated labor and materials costs associated with maintaining the 

operation of the equipment. 

• Repair costs: DOE calculated the cost for repairs as an annual expense, representing

the estimated labor and materials costs associated with repairing or replacing 

components that have failed.

• Equipment lifetime: The typical age at which the ACIM equipment is retired from 

service.

• Discount rate: The rate at which future costs are discounted to establish their present 

value.

Figure 8.1.1 depicts the relationships between the installed cost and operating cost inputs 

for the calculation of the LCC and PBP and how those costs vary with increasing efficiency.

Table 8.1.1 summarizes the characteristics of the inputs to the LCC and PBP analysis, and lists 

the corresponding reference chapter in the final rule TSD for details on the calculation of the 

inputs.

8-5
Figure 8.1.1 Flow Diagram of Inputs for the Determination of Life-Cycle Cost and Payback 

Period

Baseline

Manufacturer

Selling Price

Std-Level

Manufacturer

Selling Price

Installation

Cost

From 

Engineering 

Analysis

(Price is a 

Function of 

Efficiency)

From Markups 

for Equipment 

Price 

Determination 

Analysis

Repair Cost

Maintenance

Cost

Lifetime

Discount Rates

Electricity and 

Water Price 

Trends

From Energy Use 

Analysis 

Energy and 

Water 

Consumption

Electricity and

Distributor

Markup

Sales Tax

Mechanical

Contractor

Markup

Customer

Price

Data Inputs

Intermediate Analysis

Output Results

Total Installed

Cost

Annual

Operating

Expense

Lifetime

Operating

Expense

Payback

Period

Life-Cycle

Cost

Water Prices

Annual Energy 

and Water Cost

8-6
Table 8.1.1 Summary Information of Inputs for the Determination of Life-Cycle Cost and 

Payback Period

Input Description TSD Chapter 

Reference

Total Installed Cost Primary Inputs

Baseline MSP Varies with equipment class. Chapter 5

Efficiency level MSP

increases

Vary with equipment class and efficiency level within 

an equipment class.

Chapter 5

Markups and sales tax Markups vary with distribution channel and sales tax 

varies with location (state) where equipment is installed.

Chapter 6

Installation price Varies with equipment class and state. Chapter 8

Operating Cost Primary Inputs

Equipment electricity 

and water consumption

Varies with equipment class and efficiency level within 

an equipment class.

Chapters 5, 7

Electricity prices Vary with location, building type. Chapter 8

Water/wastewater 

prices

Vary with location. Chapter 8

Electricity and 

water/wastewater price 

trends

Vary with location and price scenario. Chapter 8

Maintenance costs Vary with equipment class. Chapter 8

Repair costs Vary with equipment class and efficiency level within 

an equipment class.

Chapter 8

Lifetime Mean assumed to be 8.5 years for all equipment. Chapters 3, 8

Discount rate Varies with type of business. Chapter 8

All of the inputs depicted in Figure 8.1.1 and summarized in Table 8.1.1 are discussed in 

sections 8.2 and 8.3.

8.2 LIFE-CYCLE COST INPUTS

8.2.1 Definition

Life-cycle cost is the total customer cost over the life of a unit of equipment, including 

equipment cost, installation cost, and operating costs (energy and water costs, maintenance costs, 

and repair costs). DOE discounted future operating costs to the time of purchase and summed 

costs over the lifetime of the equipment. Life-cycle cost is defined by the following equation:

∑=

= + +

N

t

t

t LCC IC OC r

1

/(1 )

Eq. 8.1

8-7
Where:

LCC = life-cycle cost ($),

IC = total installed cost ($),

N = lifetime of equipment (years),

b

OCt = operating cost ($) of the equipment in year t,

r = discount rate, and

t = year for which operating cost is being determined.

Because DOE gathered most of its data for the LCC analysis in 2013, DOE expressed all 

costs in 2013$. Total installed cost, operating cost, lifetime, and discount rate are discussed in 

the following sections. In the LCC analysis, DOE assumed that the first year of equipment 

purchase is 2018, the compliance date for standards set in this rulemaking.

8.2.2 Total Installed-Cost Inputs

The following equation defines the total installed cost:

IC = EQP + INST

Eq. 8.2

Where:

EQP = customer purchase price for the equipment ($), and

INST = installation cost or the customer price to install equipment ($).

DOE based the equipment price on the distribution channel through which the customer 

purchases the equipment, as discussed in final rule TSD chapter 6.

The remainder of this section provides information about the variables DOE used to 

calculate the total installed cost for ACIM equipment. Table 8.2.1 shows the inputs used to 

determine total installed cost.

Table 8.2.1 Inputs for Total Installed Costs

Baseline manufacturer selling price ($)

Price trend factor

Efficiency level manufacturer selling price increases ($)

Wholesaler markup

Contractor markup

National account markup

Sales tax ($)

Installation cost ($)

b Though the average equipment life is 8.5 years, the model uses a range of years to calculate the equipment lifetime. 

8-8

 
8.2.2.1 Baseline Energy Consumption and Manufacturer Selling Price

The baseline MSP is the price manufacturers charge for equipment just meeting the 

existing minimum efficiency (or baseline) standards or base-case efficiency levels (for 

equipment classes with no standards). The MSP includes a markup that is applied to convert

MPC to an MSP. DOE developed MSP values for the primary equipment classes (see final rule 

TSD chapter 5 for a discussion of primary versus secondary classes). Table 8.2.2 lists the

primary equipment classes that DOE evaluated during the final rule analysis of the current 

rulemaking.

Ten of the equipment classes in Table 8.2.2 are subject to standards set by the Energy 

Policy and Conservation Act (EPCA) as amended by EPACT 2005, and the other four equipment 

classes have not yet been subject to a standard set by legislation or by DOE. In the cases of three 

large batch equipment classes—IMH-W-Large-B, IMH-A-Large-B, and RCU-Large-B—two 

representative units were modeled in the LCC model. One unit represents the low end of the 

harvest capacity range covered by the equipment class while the second represents a unit higher 

in the harvest capacity and at a capacity representative of the larger machines currently available. 

To provide illustrative class-level inputs and results, weighted averages were created from the 

typical units’ values using historical shipments data. Note that the weighted averages are 

illustrative only, and all analyses were conducted using the typical units. The weighting factors 

are provided in TSD chapter 7.

8-9
Table 8.2.2 Equipment Classes Evaluated for the Automatic Commercial Ice-Making

Equipment Standard Life-Cycle Cost and Payback Period Analysis

Abbreviation Equipment Type Condenser 

Type

Harvest Rate

lb ice/24 hours Ice Type

IMH-W-Small-B Ice-Making Head Water <500 Batch

IMH-W-Med-B Ice-Making Head Water ≥500 and 

<1,436 Batch

IMH-W-Large-B* Ice-Making Head Water ≥1,436 and 

<4,000 Batch

IMH-A-Small-B Ice-Making Head Air <450 Batch

IMH-A-Large-B*,**

(also IMH-A-Large-B1) Ice-Making Head Air ≥450 and <800† Batch

IMH-A-Extended-B*,**

(also IMH-A-Large-B2) Ice-Making Head Air ≥800± and <4,000 Batch

RCU-Large-B*,‡ 

Remote Condensing, 

not Remote 

Compressor

Air ≥1,000 and 

<4,000 Batch

SCU-W-Large-B Self-Contained Unit Water ≥200 and 

<4,000 Batch

SCU-A-Small-B Self-Contained Unit Air <175 Batch

SCU-A-Large-B Self-Contained Unit Air ≥175 and 

<4,000 Batch

IMH-A-Small-C Ice-Making Head Air <700 Continuous

IMH-A-Large-C Ice-Making Head Air ≥700 and 

<4,000 Continuous

RCU-Small-C

Remote Condensing, 

not Remote 

Compressor

Air <850 Continuous

SCU-A-Small-C Self-Contained Unit Air <700 Continuous

*IMH-W-Large-B, IMH-A-Large-B, and RCU-NRC-Large-B were modeled as two different typical sizes, one 

at the lower end of the harvest range and one near the higher end of the harvest range where a significant number 

of units are available. In the LCC model, the low and high harvest rate models were denoted simply as B1 and 

B2. Where appropriate, the analyses add or perform weighted averages of the two typical sizes to present class 

level results. The weights used are presented in TSD chapter 7.

**IMH-A-Large-B was established by EPACT 2005 as a class between 450 and 2,500 lb ice/24 hours. For 

analysis, DOE divided this into two typical sizes, “Large” and “Very Large” or “Medium” and “Large.” In the 

LCC and national impact analysis modeling, the units were denoted as B1 and B2.

† Upper limit corresponding with the proposed standard level.

‡ RCU-Large-B equipment was combined into one class. The classes were disaggregated to develop standards 

by inclusion of an energy allowance related to lines carrying refrigerants.

Table 8.2.3 presents the baseline energy consumption values and the baseline MSPs used 

in the LCC and PBP analysis for the representative sizes for each of the primary equipment 

classes and the typical units for the three large batch classes. Final rule TSD chapter 5 explains 

how energy use and MSP vary by harvest capacity. Table 8.2.3 also identifies whether DOE 

obtained the baseline from the engineering analysis, for equipment with no existing standards, or 

set it at the standards baseline, for equipment covered under existing standards. As discussed in 

chapter 5, for new equipment on the market today that is not covered by existing U.S. standards, 

DOE set the baseline at levels reflecting the least-efficient equipment that could potentially be 

available. Baseline energy consumption values presented in this section provide energy use per 

100 pounds of ice produced. Chapter 7 discusses the methodology to calculate annual energy 

consumption using these values. 

8-10
Table 8.2.3 Baseline Energy Consumption Levels and Estimated MSP Values for the 

Primary Equipment Classes

Equipment Class

Baseline Energy 

Consumption

kWh/100 lb ice

Manufacturer 

Selling Price Baseline Type

IMH-W-Small-B 6.14 $1,122 Standards Baseline

IMH-W- Med -B 4.65 $1,931 Standards Baseline

IMH-W-Large-B 4.00 $2,738 Standards Baseline

 IMH-W-Large-B1 4.00 $2,136 Standards Baseline

 IMH-W-Large-B2 4.00 $4,684 Standards Baseline*

IMH-A-Small-B 7.68 $1,144 Standards Baseline

IMH-A-Large-B 5.89 $1,898 Standards Baseline

 IMH-A-Large-B1 6.01 $1,867 Standards Baseline

 IMH-A-Large-B2 5.24 $2,065 Standards Baseline

RCU-Large-B 5.10 $2,560 Standards Baseline

 RCU-Large-B1 5.10 $2,463 Standards Baseline

 RCU-Large-B2 5.10 $4,003 Standards Baseline

SCU-W-Large-B 7.60 $1,530 Standards Baseline

SCU-A-Small-B 12.84 $1,314 Standards Baseline

SCU-A-Large-B 9.80 $1,402 Standards Baseline

IMH-A-Small-C 9.05 $3,382 Engineering Baseline

IMH-A-Large-C 6.60 $2,613 Engineering Baseline

RCU-Small-C 6.33 $2,613 Engineering Baseline

SCU-A-Small-C 10.12 $1,764 Engineering Baseline

* The existing standard of 4.00 kWh/100 lb ice applies to equipment up to 2,500 lb/day, while this typical 

unit is a 2,600 lb/day machine.

8.2.2.2 Efficiency Level Energy Consumption and Manufacturer Selling Price 

Increases

The efficiency level MSP increase is the change in MSP associated with producing 

equipment at higher efficiency levels. DOE determined increases in MSP as a function of 

equipment efficiency for each of the primary equipment classes. The engineering analysis 

established a series of MSP increases for each standard level. Table 8.2.4 presents the increase in 

MSP corresponding to each efficiency level for each primary equipment class.

Table 8.2.5 presents the energy consumption of the representative units belonging to each 

of the primary equipment classes and the typical units for the three large batch classes that DOE 

selected for the engineering analysis. As discussed in chapter 7, ice makers are assumed to 

operate in ice-making mode 42 percent of the time and in standby mode 58 percent of time. In 

standby mode, all equipment was assumed to use 5 watts continuously, or roughly 0.07 kWh per 

day.

8-11
Table 8.2.4 Baseline and Incremental Manufacturer Selling Prices for the Primary 

Equipment Classes*

Equipment Class

Baseline

Level 

1

Increase in MSP by Efficiency Level 2013$

Level 

2

Level 

3

Level 

3A

Level 

4

Level 

4A

Level 

5

Level 

6

Level 

7

IMH-W-Small-B 1,122 57.54 103.78 301.18 446.89 465.92 

IMH-W- Med -B 1,931 83.55 255.17 511.99 

IMH-W-Large-B** 2,738 555.64 

 IMH-W-Large-B1 2,136 524.01 

 IMH-W-Large-B2 4,684 657.84 

IMH-A-Small-B 1,144 74.36 157.23 187.52 174.32 481.94 487.05 

IMH-A-Large-B** 1,898 132.42 202.01 

 IMH-A-Large-B1 1,867 57.82 129.52 133.81 197.17 476.82 

 IMH-A-Large-B2 2,065 534.28 592.54 

RCU-Large-B** 2,560 91.99 360.33 610.85 

 RCU-Large-B1 2,463 74.30 317.89 585.17 

 RCU-Large-B2 4,003 356.30 994.72 

SCU-W-Large-B 1,530 55.89 86.17 116.81 150.76 450.95 

SCU-A-Small-B 1,314 30.59 48.12 65.64 121.56 184.47 458.55 

SCU-A-Large-B 1,402 36.39 56.16 90.33 133.58 435.89 

IMH-A-Small-C 3,382 40.89 67.55 95.18 487.18 491.52 

IMH-A-Large-C 2,613 37.52 60.31 609.71 627.14 

RCU-Small-C 2,613 37.18 89.55 127.34 652.44 673.91 

SCU-A-Small-C 1,764 17.65 32.43 47.68 467.06 477.69 

* Blank cells imply there are no associated efficiency levels.

** Values for IMH-W-Large-B, IMH-A-Large-B, and RCU-Large-B are weighted averages of the two sub-equipment class 

level typical units shown in the table and using weights provided in TSD chapter 7.

Table 8.2.5 Energy Consumption Values for the Primary Equipment Classes, by Efficiency 

Level*

Product Class

Total Energy Usage (kWh/100 lb ice)

Level 

1

Level 

2

Level 

3

Level 

3A

Level 

4

Level 

4A

Level 

5

Level 

6

Level 

7

IMH-W-Small-B 6.14 5.53 5.22 4.91 4.82 4.68

IMH-W- Med -B 4.65 4.18 3.95 3.80

IMH-W-Large-B

 IMH-W-Large-B1 4.00 3.67

 IMH-W-Large-B2 4.00 3.70

IMH-A-Small-B 7.68 6.91 6.53 6.29 6.14 5.76 5.72

IMH-A-Large-B

 IMH-A-Large-B1 6.01 5.41 5.11 5.06 4.81 4.60

 IMH-A-Large-B2 5.24 4.72 4.62

RCU-Large-B

 RCU-Large-B1 5.10 4.59 4.34 4.22

 RCU-Large-B2 5.10 4.59 4.39

SCU-W-Large-B 7.60 6.84 6.46 6.08 5.70 5.34

SCU-A-Small-B 12.84 11.56 10.91 10.27 9.63 8.99 8.64

SCU-A-Large-B 9.80 8.82 8.33 7.84 7.35 6.94

IMH-A-Small-C 9.05 8.15 7.69 7.24 6.79 6.72

IMH-A-Large-C 6.60 5.94 5.61 5.28 5.06

RCU-Small-C 6.33 5.70 5.38 5.06 4.75 4.65

SCU-A-Small-C 10.12 9.10 8.60 8.09 7.59 7.43

* Blank cells imply there are no associated efficiency levels.

8-12
8.2.2.3 Overall Markup

As discussed in the final rule TSD chapter 6, Markups for Equipment Price 

Determination, DOE calculated overall markup and applied it to the equipment MSP to calculate 

the equipment purchase price for customers. DOE calculated baseline markups to convert 

baseline MSP to baseline customer purchase price and incremental markups to convert the 

increments in MSP to increments in customer purchase price. DOE used these markup values in 

the LCC and PBP analysis for calculation of baseline and higher efficiency equipment price to 

customers. 

8.2.2.4 Installation Cost

Most automatic commercial ice makers are installed in fairly standard configurations, 

which helps in estimating the cost of installation. DOE defines installation cost for equipment 

going into a non-space-constrained application as the sum of the base installation cost (materials 

and labor), and as-required, additional installation costs addressing two efficiency increments 

(represented by specific design options) as discussed below.

The base installation cost for each equipment class was arrived at based on conversations 

with ice maker installing contractors and RS Means data.

3 The first efficiency increment was the 

inclusion of drain water heat exchangers (DWHXs)—an additional installation cost was added at 

the efficiency level at which a DWHX is added to the engineering curve, and for all higher 

efficiency levels. The DWHX installation cost was calculated as the product of hourly labor cost 

for an installer and the additional hours required to install the DWHX. Based on a review of 

DWHX literature, DOE estimated the additional installation time for a DWHX in conjunction 

with the ice maker installation to be 0.75 hours. Based on RS Means data,

3 the labor rate was 

estimated to be $75.65 per hour. For the national impact analysis (NIA), the additional DWHX

installation cost was applied to all installations including a DWHX throughout the 30-year 

analysis period. 

For the two larger remote condensing equipment classes (RCU-Large-B2 and RCULarge-B1), DOE included additional installation costs for efficiency levels incorporating 

increased condenser size. Based on review of RS Means data,

3 for the RCU-Large-B1 equipment 

class, DOE estimated an additional 0.5 hours of labor to install the larger and heavier condenser 

but with no additional cost for materials. For the RCU-Large-B2 equipment class, DOE 

estimated an additional 1.5 hours of labor to install the heavier condenser and an average of $40 

in materials for modifications to the base mounting. The labor rate is $79.50 per hour. For the 

RCU-Large-B2 equipment class, the additional material costs were assumed to apply to 

replacement of existing equipment, one time, while the labor costs for both classes were assumed 

to apply to all installations with increased condenser size throughout the 30-year analysis period.

For equipment classes where increased cabinet size was considered in the engineering 

curves and for which a portion of customers will face space constraints, additional installation 

costs were estimated. For the three IMH equipment classes with 22-inch, reduced-footprint

equipment, for customers to “go beyond” the end of the 22-inch space constrained engineering 

curve to the higher efficiency levels available in the non-22-inch space constrained engineering 

curve, a certain percentage of customers would likely need to retrofit the existing space to make 

8-13
possible the installation of larger units. For the non-22-inch products, in equipment classes in 

which the engineering curve identified a necessary size increase, a small percentage of customers 

would also need to retrofit the existing space to install the larger units. The installation costs for 

equipment classes with space constraints are the weighted average of the installation costs of the 

non-space-constrained and the space-constrained units for the equipment class under 

consideration. 

Calculation of the construction cost expense reflects the following assumptions and data:

• Additional construction costs to address space constraints are applicable only to 

replacement shipments

• The relative shipment fraction of 22-inch units in each of the three classes

• The percentage of customers buying 22-inch units who have space constraints 

• The percentage of customers buying non-22-inch units who have space constraints

• The average construction cost for installation of the units where space constraints 

exist (e.g., to raise a small area of a ceiling).

Some customers buying 22-inch units would be able to purchase taller units, while others 

would incur construction costs to do so. To merge the 22-inch and non-22-inch curves, the 

construction cost is introduced at the end of the 22-inch engineering curve to “enable” spaceconstrained customers to buy bigger units. For customers of non-22-inch units, space constraints 

occur when the design option is added that increases the cabinet size. All inputs to the 

calculation for 22-inch units are shown in Table 8.2.6 while the inputs for the calculation of non22-inch units are shown in Table 8.2.7. The construction cost adders are one-time adders, 

meaning that once a building is renovated one time the adder is no longer needed. Thus, for use 

in the NIA, the installation cost adders are eliminated after the base year stock is retired for the 

first time. For the LCC/PBP analysis, the total installation costs for all equipment classes are 

shown in Table 8.2.8.

Table 8.2.6 Installation Cost Assumptions for 22-Inch Units

IMH-ASmall-BIMH-ALarge-B1IMH-WSmall-B

Replacement shipment fraction 85.8% 85.8% 85.8%

Estimated construction cost for customers with 

size constraints $1,804.0 $1,804.0 $1,804.0 

Total 22-inch-wide shipments 13.0% 5.0% 48.0%

Estimated space-constrained 22-inch-wide 

shipments 30.0% 35.0% 30.0%

Efficiency level above which construction cost is 

added to merge curves 18.1% 15.6% 21.5%

8-14
Table 8.2.7 Installation Cost Assumptions for Non-22-Inch Units with Size Growth

IMH-ASmall-BIMH-ALarge-B1IMH-WSmall-B

Replacement shipment fraction 85.8% 85.8% 85.8%

Estimated construction cost for customers with 

size constraints $1,804.0 $1,804.0 $1,804.0 

Total non-22-inch-wide size shipments 87.0% 95.0% 52.0%

Estimated space-constrained non-22-inch wide 

shipments 10.0% 15.0% 10.0%

Efficiency level above which construction cost is 

added to merge curves 18.1% 19.2% 19.2%

Note that on all of the tables showing assumptions by efficiency levels, levels 3A and 4A,

were added to denote the end of the 22-inch engineering curves. Thus, for the IMH-Air Batch 

product classes, a level 3A was added at the end of the space-constrained 22-inch curve at an 18 

percent efficiency improvement above the baseline for the small units (IMH-A-Small-B) and 16 

percent above the base for the large units (IMH-A-Large-B1). Similarly, a level 4A was added to 

reflect the end of the space constrained IMH-W-Small-B curves. Above these levels the 

equipment class curves are merged, but with the additional construction costs added to the 

installation costs, as necessary, to remove space constraints. These 3A and 4A efficiency levels 

do not apply to the other classes. While not identified as having significant 22-inch shipments in 

the NOPR comments, it is noted that a revised engineering curve for IMH-A-Small-C also 

reflected increased size growth and was treated similarly to the non-22 inch IMH-A-Small-B 

equipment.

Table 8.2.8 provides a list of the estimated U.S. average installation cost for each primary 

equipment class.

Table 8.2.8 Installation Cost by Equipment Class, U.S. Average

Equipment Class 

Installation Cost (2013$)

Level 

1

Level 

2

Level 

3

Level 

3A

Level 

4

Leve

l 4A

Level 

5

Level 

6

Level 

7

IMH-W-Small-B 305 305 305 412 442 665 

IMH-W-Med-B 609 609 666 666 

IMH-W-Large-B* 1,015 1,072 

 IMH-W-Large-B1 1,015 1,072 

 IMH-W-Large-B2 1,015 1,072 

IMH-A-Small-B 305 305 312 312 500 556 556 

IMH-A-Large-B* 609 618 620 620 827 875 

 IMH-A-Large-B1 609 609 612 612 857 913 

 IMH-A-Large-B2 609 666 666 

RCU-Large-B* 1,523 1,523 1,627 1,627 

 RCU-Large-B1 1,523 1,523 1,619 1,619 

 RCU-Large-B2 1,523 1,523 1,738 

SCU-W-Large-B 609 609 609 609 609 666 

SCU-A-Small-B 305 305 305 305 305 305 361 

SCU-A-Large-B 609 609 609 609 609 666 

* Values for IMH-W-Large-B, IMH-A-Large-B, and RCU-Large-B are weighted averages of the two sub-equipment 

class level typical units shown in the table and using weights provided in TSD chapter 7.

8-15
Table 8.2.8 Installation Cost by Equipment Class, U.S. Average

Equipment Class 

Installation Cost (2013$)

Level 

1

Level 

2

Level 

3

Level 

3A

Level 

4

Leve

l 4A

Level 

5

Level 

6

Level 

7

IMH-A-Small-C 305 305 305 305 305 305 

IMH-A-Large-C 609 609 609 609 609 

RCU-Small-C 761 761 761 761 761 761 

SCU-A-Small-C 305 305 305 305 305 305 

* Values for IMH-W-Large-B, IMH-A-Large-B, and RCU-Large-B are weighted averages of the two sub-equipment 

class level typical units shown in the table and using weights provided in TSD chapter 7.

Table 8.2.9 shows installation cost indices for installation costs in each of the 50 states

and the District of Columbia, and the weighted average for the entire United States. These are

used to adjust the national installation cost to reflect differences by state. The state-level data are 

based on data published by RS Means for major U.S. cities.

4 To arrive at an average index for 

each state, DOE first weighted the city indices up to state-level values using the relative city-tototal population values, using the 2013 population in each city.

5 DOE used state-level population 

weights for 2013 from the U.S. Census Bureau6 to calculate a weighted-average index for each 

state from the RS Means census data.

Table 8.2.9 Installation Cost Indices (National Value = 100.0)

State Index State Index State Index

Alabama 82.5 Kentucky 95.2 North Dakota 87.4

Alaska 104.6 Louisiana 86.9 Ohio 99.3

Arizona 94.3 Maine 92.2 Oklahoma 84.6

Arkansas 83.8 Maryland 95.2 Oregon 103.7

California 112.9 Massachusetts 110.1 Pennsylvania 110.8

Colorado 93.3 Michigan 102.3 Rhode Island 106.1

Connecticut 109.2 Minnesota 107.6 South Carolina 83.8

Delaware 109.2 Mississippi 84.7 South Dakota 81.1

Dist. of Columbia 99.8 Missouri 102.2 Tennessee 92.4

Florida 88.5 Montana 91.4 Texas 85.2

Georgia 89.3 Nebraska 93.6 Utah 89.9

Hawaii 106.6 Nevada 101.7 Vermont 91.7

Idaho 91.9 New Hampshire 97.0 Virginia 91.2

Illinois 115.6 New Jersey 112.0 Washington 104.9

Indiana 94.2 New Mexico 90.7 West Virginia 99.0

Iowa 94.8 New York 126.4 Wisconsin 101.7

Kansas 91.1 North Carolina 83.9 Wyoming 88.4

8.2.2.5 Equipment Price Trends

In prior energy conservation standards rulemakings, DOE estimated the total installed 

costs per unit for equipment, and then assumed that costs remain constant throughout the 

analysis period. This assumption is conservative because installed costs tend to decrease over 

time. In 2011, DOE issued a notice of data availability (NODA) titled Equipment Price 

Forecasting in Energy Conservation Standards Analysis. 76 FR 9696 (Feb. 22, 2011). In the 

NODA, DOE proposed a methodology for determining whether equipment prices have trended 

downward in real terms. The methodology examines so-called price or experiential learning, 

8-16
wherein, with ever-increasing experience with the production of a product, manufacturers are 

able to reduce their production costs through innovations in technology and process.

To account for increased efficiency in the automatic commercial ice-making equipment 

manufacturing process over time, DOE used a price forecast methodology based on experiential 

learning (see appendix 8D for more information on experiential learning). For the LCC model, 

the impact of experiential learning was a decrease in the MSP to account for changes from the 

time prices were developed (2013) until the start of the LCC analysis (2018). The price trend

factor used in the LCC and PBP analysis was 0.9952, which means MSPs shown on Table 8.2.4

were reduced by 0.48 percent in the development of total installed costs.

8.2.2.6 Weighted Average Total Installed Cost

As presented in Eq. 8.2 (see section 8.2.2), the total installed cost is the sum of the 

equipment price and the installation cost. DOE derived the customer equipment price for any 

given standard level by multiplying the baseline MSP by the baseline markup and adding to it the 

product of the incremental MSP and the incremental markup. Because MSPs, markups, and the 

sales tax all can take on a variety of values, depending on location, the resulting total installed 

cost for a particular standard level will not be a single-point value, but rather a distribution of 

values.

The weighted average costs for the IMH-A-Small-B equipment class are presented below 

for the baseline level at national average markup rates and national average installation costs for 

illustration purposes. DOE used the baseline MSP and the standard-level MSP increases as the 

starting points for determining the total installed cost (values are taken directly from Table 8.2.3

and Table 8.2.4). DOE used the baseline and incremental markups, the sales tax, and installation 

cost to convert the MSPs into total installed costs for cases where the incremental installation 

cost is held flat. As an example, Table 8.2.10 summarizes the weighted average or mean costs 

and markups necessary for determining the weighted average baseline and standard-level total 

installed costs.

Table 8.2.10 Costs and Markups for Determination of Weighted Average Total Installed 

Costs (IMH-A-Small-B)

Variable Weighted Average or Mean Value

Baseline MSP $1,144.41 

Standard-Level MSP Increase (Efficiency Level 4) $174.32

Baseline Markup Factor with Sales Tax 1.8764

Incremental Markup Factor with Sales Tax 1.3153

Installation Cost–Baseline* $304.53

Installation Cost – Efficiency Level 4 $499.56

Installation Cost Factor, for U.S. Average 1.000

Price Trend Factor** 0.9952

* Installation cost applied to the baseline unit, with no incremental installation cost.

** Price trend (learning) factor for 2018. See appendix 8D.

8-17
Eq. 8.3 for the baseline (level 1) and for a higher efficiency level (level 4) IMH-A-Small-B 

equipment class illustrates how DOE derived the weighted average total installed cost based on 

the data shown in Table 8.2.10. For the baseline product, DOE calculated the total installed cost 

at national average conditions as follows:

c

IIIIBBBBBBBB IIIIII− − SSSSSSSS−BB = EEEEEEBBBBBBBB IIIIII− − SSSSSSSS−BB + IIIIIIIIBBBBBBBB IIIIII− − SSSSSSSS−BB × IIIIIIIIIIII II

= MMMMMMBBBBBBBB IIIIII− − SSSSSSSS−BB × PPPPPPPP TTTT FF FFFF AA AAAAAAAA YYYY YY

× MMMMBBBBBBBB IIIIII− − SSSSSSSS−BB + IIIIIIIIBBBBBBBB IIIIII− − SSSSSSSS−BB

× IIIIIIIIIIII II

= $1,144.41 × 0.9952 × 1.8764 + $304.53 × 1.00

= $2,137.15+ $304.53

= $2,441.68

Eq. 8.3

Where:

ICBASE IMH-A-Small-B = total installed cost of IMH-A-Small-B equipment at baseline efficiency level 

($), 

EQPBASE IMH-A-Small-B = equipment purchase price of IMH-A-Small-B equipment at baseline 

efficiency level ($),

INSTBASE IMH-A-Small-B = installation cost of IMH-A-Small-B equipment at baseline efficiency level 

($),

MFGBASE IMH-A-Small-B = MSP of IMH-A-Small-B equipment at baseline efficiency level ($), 

Price Trend Factor Analysis Year = Price trend factor for the 2013–2018 period, or 0.9952,

MUBASE IMH-A-Small-B = overall baseline markup for equipment class IMH-A-Small-B, 1.8764,

including the national average state tax factor, and

ISTINDEX = location dependent multiplier on installation cost; equals 1.0 at the national 

average.

DOE calculated the total installed cost for the higher efficiency level (efficiency level 4)

using an incremental MSP. DOE applied an incremental markup factor to the incremental 

increases in MSP. The level 4 price is equal to the baseline price calculated above, plus the MSP

increment for a higher efficiency level, multiplied by the incremental markup.

c Note that the numbers shown in Eq. 8.3 have been rounded and do not exactly match the numbers in the analysis.

8-18

 
As an example, DOE calculated the national average, level 4, total installed cost (ICIMH-ASmall-BLEVEL4) as follows:d

IIIIIIIIII− − SSSSSSSS−BB LL LL LL4

= EEEEEEIIIIII− − SSSSSSSS−BB LL LL LL4 + IIIIIIIIIIIIII− − SSSSSSSS−BB LL LL LL4 × IIIIIIIIIIII II

=

MMMMMMBBBBBBBB IIIIII− − SSSSSSSS−BB × PPPPPPPP TTTT FF FFFF AA AAAAAAAA YYYY YY ×

MMMMBBBBBBBB IIIIII− − SSSSSSSS−BB +

∆MMMMMMIIIIII− − SSSSSSSS−BB LL LL LL4 × PPPPPPPP FF FFFF AA AAAAAAAA YYYY YY ×

MMMMIIIIII− − SSSSSSSS−BB LL LL LL4 + IIIIIIIIIIIIII− − SSSSSSSS−BBLL LL LL4 × IIIIIIIIIIII II

= $1,144.41 × 0.9952 × 1.8764 + $174.32 ×0.9952 × 1.3153 +

$499.56 × 1.000

= $2,864.81

Eq. 8.4

Where:

ICIMH-A-Small-B LEVEL4 = total installed cost of IMH-A-Small-B equipment at efficiency level 4 ($), 

EQP IMH-A-Small-BLEVEL4 = equipment price of IMH-A-Small-B equipment at efficiency level 4 ($), 

INST IMH-A-Small-BLEVEL4 = installation cost of IMH-A-Small-B equipment at efficiency level 4 ($),

ΔMFG IMH-A-Small-BLEVEL4 = incremental increase in MSP of IMH-A-Small-B equipment at 

efficiency level 4 compared to equipment at baseline efficiency level ($), and

MU IMH-A-Small-BLEVEL4 = incremental markup for equipment class IMH-A-Small-B, including state 

tax factor.

Table 8.2.11 presents the weighted average equipment price, installation cost, and total

installed costs for the IMH-A-Small-B equipment classes at the baseline level and each higher

efficiency level examined.

Table 8.2.11 Weighted Average Equipment Price, Installation Cost, and Total Installed 

Costs for IMH-A-Small-B at U.S. Average Conditions (2013$)

Efficiency Level Equipment Price (MSP) Installation Cost Total Installed Cost*

1 (Baseline) $1,144.41 $304.53 $2,441.68 

2 $1,218.77 $304.53 $2,539.01 

3 $1,301.64 $311.91 $2,654.87 

4 $1,331.93 $311.91 $2,694.52 

5 $1,318.74 $499.56 $2,864.89 

6 $1,626.35 $556.30 $3,324.29 

7 $1,631.47 $556.30 $3,330.99 

* Numbers provided in this table are taken directly from the LCC analysis, and thus may differ from those provided above 

in the text due to rounding.

d The numbers shown in Eq. 8.4 have been rounded and do not exactly match the numbers used in the analysis.

8-19

 
8.2.3 Operating-Cost Inputs

DOE defines the operating cost as the sum of energy cost, water cost, repair cost, and 

maintenance cost, or:

OC = EC+ WC+ RC+ MC

Eq. 8.5

Where:

OC = operating cost ($), 

EC = energy cost ($),

WC= water cost ($),

RC = repair cost ($), and

MC = maintenance cost ($).

The remainder of this section describes the variables that DOE used to calculate 

operating costs for automatic commercial ice makers. Table 8.2.12 shows the inputs used to

determine operating costs.

Table 8.2.12 Inputs Used to Determine Operating Costs

Electricity price (cents/kWh)

Water/wastewater price ($/1,000 gallons)

Electricity and water price trends

Repair cost ($)

Maintenance cost ($)

Lifetime (years)

Discount rate (%)

Effective date of standard

Baseline electricity consumption (kWh/100 lb ice)

Baseline water consumption (gallons of water/100 lb ice)

Standard case electricity consumption (kWh/100 lb ice)

Standards water consumption (gallons of water/100 lb ice)

8.2.3.1 Electricity Price Analysis

This section describes the electricity price analysis used to develop the energy portion of 

the annual operating costs (price multiplied by electricity consumption) for automatic 

commercial ice makers used in various commercial building types. 

Subdivision of the Country. Because of the wide variation in electricity consumption 

patterns, wholesale costs, and retail rates across the country, DOE considered regional 

differences in electricity prices. DOE divided the United States into the 50 states and the District 

of Columbia. DOE used average effective commercial electricity prices at the state level from 

the EIA web page titled Sales (Consumption), Revenue, Prices and Customers.

7 At the time 

DOE developed the LCC/PBP model, the latest available data was for calendar year 2013 Table 

8.2.13 provides the 2013 electricity prices.

8-20
Table 8.2.13 Commercial Electricity Prices by State (2013 cents/kWh)

State

Commercial 

Electricity Price 

cents/kWh

State

Commercial 

Electricity Price 

cents/kWh

State

Commercial 

Electricity Price 

cents/kWh

Alabama 10.50 Kentucky 8.50 North Dakota 8.31

Alaska 15.62 Louisiana 8.94 Ohio 9.38

Arizona 9.87 Maine 11.72 Oklahoma 7.71

Arkansas 7.98 Maryland 10.70 Oregon 8.39

California 14.57 Massachusetts 14.51 Pennsylvania 9.26

Colorado 9.87 Michigan 11.07 Rhode Island 13.08

Connecticut 14.64 Minnesota 9.53 South Carolina 9.82

Delaware 10.26 Mississippi 10.21 South Dakota 8.44

Dist. of Col. 11.93 Missouri 8.72 Tennessee 10.01

Florida 9.49 Montana 9.52 Texas 8.03

Georgia 9.84 Nebraska 8.62 Utah 8.37

Hawaii 34.06 Nevada 9.02 Vermont 14.64

Idaho 7.40 New Hampshire 13.52 Virginia 8.05

Illinois 7.88 New Jersey 12.80 Washington 7.76

Indiana 9.48 New Mexico 9.78 West Virginia 8.16

Iowa 8.47 New York 15.23 Wisconsin 10.84

Kansas 9.54 North Carolina 8.73 Wyoming 8.60

DOE recognized that different kinds of businesses typically use electricity in different 

amounts at different times of the day, week, and year, and therefore experience different 

effective prices. To make this adjustment, DOE used the 2003 Commercial Buildings Energy 

Consumption Survey (CBECS)

8 data set to identify the average prices paid by the seven kinds of 

businesses in this analysis compared with the average prices paid by all commercial customers.

Because it isn’t explicitly recognized as a CBECS building type, DOE identified multi-line retail 

by identifying retail stores for which the data suggest the presence of walk-in refrigeration and 

other commercial refrigeration equipment.

e Eq. 8.6 shows how DOE calculated the prices paid 

by the seven types of businesses in each state or district:

EEEEEE EECCCC BB BBBBBB BBBB 2013 = EEEEEE EECCCC 2013 × � PP BBBBBBBBBBBBBBBB UUUU 2003

CCCCCC UUUU 2003

�

Eq. 8.6

Where:

EPRICECOM BLDGTYPE STATE 2013 = average commercial sector electricity price in a specific building 

type (such as health care, food sales, and foodservice) in a specific state in 2013,

EPRICE COM STATE 2013 = average commercial sector electricity price in a specific state in 2013,

EPRICE BLDGTYPE US 2003 = national average commercial-sector electricity price in a specific building 

type in 2003 CBECS, and

EPRICE COM US 2003 = national average commercial sector electricity price in 2003 CBECS.

Table 8.2.14 shows the resulting commercial building electricity price ratios.

e Automatic commercial ice makers are not explicitly captured by the CBECS database, so commercial refrigeration 

is used as a proxy for buildings of interest to this analysis.

8-21

 
Table 8.2.14 Derived National Average Commercial Electricity Prices and Ratios by 

Business Type

Business Type Electricity Price 

dollars/kWh

Ratio of Electricity Price to Average 

Price for all Commercial Buildings

HealthCare $0.067 85%

Lodging $0.077 97%

Foodservice $0.082 104%

Retail $0.083 105%

Education $0.075 95%

Food Sales $0.076 96%

Office $0.081 103%

All commercial buildings $0.079 100%

Source: CBECS 2003. 

The derived ratio equates commercial electricity prices by building type to the overall 

average commercial building price. DOE then combined the derived ratio with state-by-state 

commercial rates to derive a series of prices for each state and for each building type. DOE 

forecast future prices as described in section 8.2.3.2. To obtain a weighted average national price

for the business types being explicitly modeled, DOE weighted the price ratios for each business 

type by the percentage of automatic commercial ice maker shipments each represents. Shipments 

by business type are discussed in chapter 9 of the TSD.

8.2.3.2 Electricity Price Trend

The electricity price trend provides the relative change in electricity prices through the 

year 2047. Estimating future electricity prices is difficult, especially considering that there are 

efforts in many states throughout the country to restructure the electricity supply industry.

DOE applied a projected trend in regional average electricity prices to each customer’s 

energy prices based on the AEO2014 price scenarios.

f The discussion in this chapter refers to the 

reference price scenario. In the LCC analysis, DOE can analyze the following four scenarios:

1. Constant (real) energy prices at 2013 values (i.e., a constant index of 1.0)

2. AEO2014, High Economic Growth (“AEO2014 High Growth” in Figure 8.2.1)

3. AEO2014, Reference Case (“AEO2014 Reference” in Figure 8.2.1)

4. AEO2014, Low Economic Growth (“AEO2014 Low Growth” in Figure 8.2.1)

Figure 8.2.1 shows the trends for the AEO2014 reference case, high growth, and low 

growth price projections at the national level. DOE extrapolated the values for the later years, 

after 2040—the last year of the AEO2014 forecast. DOE used the price trend from 2031 through

2040 of each forecast scenario to establish prices for the years 2041 through 2047.

f The Annual Energy Outlook develops forecasts based on the nine Census Divisions. Each state is mapped to the 

appropriate census division and the corresponding growth rates are applied to the base year state commercial price.

8-22

 
Figure 8.2.1 National Electricity Price Trends for Commercial Rates to 2047

The default electricity price trend scenario used in the LCC analysis is the trend from the

AEO2014 reference case, shown in Figure 8.2.1. The LCC model spreadsheets have the 

capability to analyze the AEO2014 high growth trends, AEO2014 low growth price trends, and 

constant energy prices for additional sensitivity analysis.

8.2.3.3 Water Cost

Water costs were developed using AWWA rate survey data. Prices were extracted for 

each utility in the AWWA database for commercial sector customers using between 50,000 and 

374,000 gallons per year. Water and wastewater prices were extracted and combined for each 

observation. The number of observations by state contained in the database varies from 0 to 38. 

To make the results as representative as possible, data from two prior surveys were compiled, 

adjusted for prices escalation, and compared to data from the 2012 survey for states with few or 

no observations. The 2013 water prices are shown in Table 8.2.15.

0.08900

0.09400

0.09900

0.10400

0.10900

0.11400

0.11900

0.12400

0.12900

0.13400

2010 2015 2020 2025 2030 2035 2040 2045

US Weighted Average Real Cost/kWh 

(2013$)

Year

Electricity Price Projections

AEO 2014 Low Growth AEO 2014 Reference AEO 2014 High Growth

8-23
Table 8.2.15 Commercial Water Prices by State (2013$/1,000 gallons)

State

Commercial 

Water Price 

$/1,000 gallons

State

Commercial 

Water Price 

$/1,000 gallons

State

Commercial 

Water Price 

$/1,000 gallons

Alabama 7.21 Kentucky 7.29 North Dakota 5.52

Alaska 10.32 Louisiana 8.11 Ohio 7.91

Arizona 6.39 Maine 4.15 Oklahoma 7.13

Arkansas 4.89 Maryland 10.13 Oregon 9.24

California 6.51 Massachusetts 11.82 Pennsylvania 6.98

Colorado 5.54 Michigan 4.22 Rhode Island 8.51

Connecticut 6.23 Minnesota 7.71 South Carolina 7.05

Delaware 11.75 Mississippi 6.60 South Dakota 8.57

Dist. of Col. 9.77 Missouri 6.44 Tennessee 8.79

Florida 8.23 Montana 6.72 Texas 6.87

Georgia 8.21 Nebraska 3.72 Utah 3.13

Hawaii 7.73 Nevada 5.98 Vermont 3.61

Idaho 3.01 New Hampshire 6.90 Virginia 9.32

Illinois 6.96 New Jersey 7.86 Washington 8.80

Indiana 7.52 New Mexico 5.79 West Virginia 7.03

Iowa 4.92 New York 6.81 Wisconsin 4.85

Kansas 6.60 North Carolina 9.27 Wyoming 7.55

8.2.3.4 Water Price Trend

To identify price trends for water, DOE examined the CPI for water and sewerage 

maintenance. Consistent with other portions of the analysis like the discount rate analysis, DOE 

used a 40-year historical series. In nominal terms, the annual average growth rate in this price 

index was 6.14 percent. The overall CPI for the same period averaged 4.06 percent. For the 

analysis period, a real water price escalation of 1.99 percent was used.

To establish high and low price scenarios, DOE calculated 20-year rolling averages of the 

annual growth rates. DOE then identified the 20-year period with the highest average escalation 

rate and the 20-year period with the lowest average escalation rate. The highest 20-year period 

was the period between 1994 and 2013, and the average real escalation rate during the period 

was 2.19 percent. To create a high water price forecast, water prices were escalated at a constant 

2.19 percent. The lowest 20-year period was between 1979 and 1998, and the average real 

escalation rate during that period was 1.41 percent. To create a low water price forecast, water 

prices were escalated at a constant 1.41 percent. Reference, high and low water prices are shown 

in TSD appendix 10B.

8.2.3.5 Repair Cost

Total repair cost for each efficiency level is calculated as the sum of the replacement 

component cost and the replacement labor cost for those components that would be repaired in 

the event of component failure. For automatic commercial ice makers, DOE identified the 

components prone to failure as compressors, fan motors, and auger motors. The replacement 

component cost calculation uses MPC values from the engineering analysis. A replacement 

product cost markup of 250 percent (2.5 times the MPC) and a probability of replacement of 25 

percent for each component are used to estimate the lifetime repair cost at each efficiency level. 

8-24
The replacement component cost calculation methodology has remained the same between the 

NOPR and the final rule analyses. 

The repair labor cost was calculated as the product of hourly labor cost for a refrigeration 

technician and the number of hours required to replace the failed component. Based on 

conversations with ice maker repair contractors, the repair labor hours for a compressor failure 

only, a condenser fan motor failure only, and an auger motor failure only were estimated to be 4 

hours, 2 hours, and 2 hours, respectively. Labor cost was estimated at $125 per hour based on 

discussions with companies repairing ACIM equipment and consistent with NOPR comments. 

The repair cost assumptions are summarized in Table 8.2.16. 

Table 8.2.16 Repair Cost Assumptions

Assumptions Compressors Fan Motors Auger Motors

Repair Chain Markup 250% 250% 250%

Probability of Replacement 25% 25% 25%

Labor Required 4 hours 2 hours 2 hours

Labor Rate ($/hr) $125 $125 $125

Annualized repair costs for each efficiency level were then calculated by dividing the 

total repair cost at the considered level by the average lifetime of 8.5 years for batch and 

continuous type ice makers. Total repair costs by equipment class and efficiency level are 

summarized in Table 8.2.17

All equipment classes other than those with 22-inch variants have a single annualized 

repair cost curve. The equipment classes with 22-inch variants have two repair cost curves, one 

each for the 22-inch and the non-22-inch units. Shipment data provided by AHRI were used to 

distribute the equipment under each of these classes and to merge the two annualized repair cost 

curves and produce one single annualized repair cost curve for the equipment class. Repair costs 

are shown in Table 8.2.17.

Table 8.2.17 Repair Cost by Equipment Class

Equipment Class 

Repair Cost (2013$/year)

Level 

1

Level 

2

Level 

3

Level 

3A

Level 

4

Level 

4A

Level 

5

Level 

6

Level 

7

IMH-W-Small-B 45.01 46.53 46.53 46.53 46.53 46.53

IMH-W-Med-B 52.92 55.80 55.80 55.80

IMH-W-Large-B* 62.98 65.04

 IMH-W-Large-B1 57.86 59.66

 IMH-W-Large-B2 79.53 82.42

IMH-A-Small-B 47.32 50.55 50.55 50.55 50.55 50.55 50.55

IMH-A-Large-B* 55.96 59.73 61.18 61.18 61.24 61.24

 IMH-A-Large-B1 54.70 58.02 59.74 59.74 59.80 59.80

 IMH-A-Large-B2 62.76 68.98 68.98

RCU-Large-B* 62.99 65.46 69.11 69.11

 RCU-Large-B1 61.39 63.26 66.79 66.79

 RCU-Large-B2 86.85 98.34 103.73

* Values for IMH-W-Large-B, IMH-A-Large-B, and RCU-Large-B are weighted averages of the two sub-equipment class level 

typical units shown in the table using the weights provided in TSD chapter 7.

8-25
Table 8.2.17 (cont)

Equipment Class 

Repair Cost (2013$/year)

Level 

1

Level 

2

Level 

3

Level 

3A

Level 

4

Level 

4A

Level 

5

Level 

6

Level 

7

SCU-W-Large-B 22.06 22.06 22.06 22.06 22.06 22.06

SCU-A-Small-B 37.17 38.07 39.10 40.14 41.10 43.04 43.38

SCU-A-Large-B 45.79 46.84 48.00 48.30 50.34 50.33

IMH-A-Small-C 78.20 80.27 81.65 82.37 102.43 105.53

IMH-A-Large-C 89.34 91.55 92.06 104.69 125.18

RCU-Small-C 85.48 87.67 89.47 90.58 106.75 120.24

SCU-A-Small-C 77.27 77.43 78.52 80.27 97.03 104.48

8.2.3.6 Maintenance Cost

The maintenance cost is the cost to the customer of ensuring proper equipment operation 

even if there are no specific equipment failures (e.g., checking and maintaining refrigerant levels, 

replacing filters, checking water distribution lines for leaks, cleaning, sanitizing, and descaling).

The maintenance cost does not include the costs associated with the replacement or repair of 

components that have failed (as discussed previously).

In the final rule analyses, DOE estimated material and labor costs for preventative 

maintenance based on RS Means cost estimation dataError! Bookmark not defined. and on telephone 

conservations with contractors. DOE assumed maintenance cost would remain constant for all 

efficiency levels within an equipment class. Table 8.2.18 shows the annualized maintenance 

costs by equipment class for each efficiency level. 

Table 8.2.18 Annual Maintenance Costs by Equipment Class and Efficiency Level

Equipment Class 

Maintenance Cost (2013$/year)

Level 

1

Level 

2

Level 

3

Level 

3A

Level 

4

Level 

4A

Level 

5

Level 

6

Level 

7

IMH-W-Small-B 401 401 401 401 401 401

IMH-W-Med-B 609 609 609 609

IMH-W-Large-B* 812 812

 IMH-W-Large-B1 812 812

 IMH-W-Large-B2 812 812

IMH-A-Small-B 751 751 751 751 751 751 751

IMH-A-Large-B* 1,050 1,050 1,050 1,050 1,050 1,050

 IMH-A-Large-B1 1,050 1,050 1,050 1,050 1,050 1,050

 IMH-A-Large-B2 1,050 1,050 1,050

RCU-Large-B* 508 508 508 508

 RCU-Large-B1 508 508 508 508

 RCU-Large-B2 508 508 508

SCU-W-Large-B 609 609 609 609 609 609

SCU-A-Small-B 761 761 761 761 761 761 761

SCU-A-Large-B 1,066 1,066 1,066 1,066 1,066 1,066

* Values for IMH-W-Large-B, IMH-A-Large-B, and RCU-Large-B are weighted averages of the two sub-equipment class level 

typical units shown in the table using the weights provided in TSD chapter 7.

8-26
Table 8.2.18 (cont)

Equipment Class 

Maintenance Cost (2013$/year)

Level 

1

Level 

2

Level 

3

Level 

3A

Level 

4

Level 

4A

Level 

5

Level 

6

Level 

7

IMH-A-Small-C 761 761 761 761 761 761

IMH-A-Large-C 1,066 1,066 1,066 1,066 1,066

RCU-Small-C 305 305 305 305 305 305

SCU-A-Small-C 761 761 761 761 761 761

8.2.3.7 Lifetime

As discussed in section 8.1.2, DOE defines lifetime as the age at which a typical

automatic commercial ice maker is retired from service. DOE estimated equipment lifetime 

based on discussions with industry experts, and concluded that a typical lifetime of 8.5 years is 

appropriate for most automatic commercial ice makers. Because some equipment has remaining 

useful life, there is a market for equipment that has been removed from service. DOE

understands, however, that the salvage value to the original purchaser is generally very low, and 

thus has not taken this into account in the LCC. 

In the LCC model, lifetime was modeled as variable. A Weibull distribution developed 

for the shipment and national impact analysis model results in a set of probability of equipment 

failure by year. While the full distribution covered the entire period of year 0 through year 16, 

the low and high ends of the distribution exhibited probabilities so low that when multiplied by

10,000 (the number of model runs) the expected times the lifetime would be selected was zero. 

The LCC model used the failure year-probability of failure combinations shown in Table 8.2.19.

Table 8.2.19 Probability of Equipment Failure by Year

Years Probability

3 0.0%

4 0.2%

5 1.0%

6 4.1%

7 12.7%

8 27.7%

9 35.0%

10 17.4%

11 1.8%

12 0.0%

8.2.3.8 Discount Rate

The discount rate is the rate at which future expenditures are discounted to establish their 

present value. DOE derived discount rates for the automatic commercial ice maker analysis by 

estimating the cost of capital for the types of companies that purchase automatic commercial ice

makers. The cost of capital is commonly used to estimate the present value of cash flows to be 

derived from for a project or investment. Most companies use both debt and equity capital to 

fund investments, so their overall cost of capital is the weighted average of the cost to the 

company of equity and debt financing. 

8-27
DOE estimated the cost of equity financing by using the Capital Asset Pricing Model 

(CAPM).

9 The CAPM, among the most widely used of models that estimate the cost of equity 

financing, assumes that the cost of equity is proportional to the amount of systematic risk 

associated with a particular company. The cost of equity financing tends to be high when a 

company faces a large degree of systematic risk and it tends to be low when the company faces a 

small degree of systematic risk.

DOE determined the cost of equity financing by using several variables, including the 

risk coefficient of a company, β (beta), the expected return on “risk free” assets (Rf), and the 

additional return expected on assets facing average market risk, also known as the equity risk 

premium or ERP. The risk coefficient of a company, β, indicates the degree of risk associated 

with a given firm relative to the level of risk (or price variability) in the overall stock market.

Risk coefficients usually vary between 0.5 and 2.0. A company with a risk coefficient of 

0.5 faces half the risk of other stocks in the market; a company with a risk coefficient of 

2.0 faces twice the overall stock market risk.

The following equation gives the cost of equity financing for a particular company:

ke = Rf + (β x ERP)

Eq. 8.7

Where:

ke = the cost of equity for a company (%), 

Rf = the expected return of the risk free asset (%),

β = the risk coefficient, and

ERP = the expected equity risk premium (%).

DOE defined the risk-free rate as the 40-year geometric average yield on long-term 

government bonds. DOE calculated the risk-free rate using Federal Reserve data for the period 

1974 through 2013,

10 with a resulting rate of 6.24 percent. DOE used a 5.81-percent estimate for 

the ERP based on the difference between a 40-year geometric average yield on the Standard & 

Poor’s 500 Index calculated using data downloaded from the Damodaran Online11 site and the 

risk-free rate.

The cost of debt financing (kd) is the interest rate paid on money a company borrows. 

DOE estimated the cost of debt by adding a risk adjustment factor (Ra) to the risk-free rate. 

d Rf Ra k = +

Eq. 8.8

Where:

kd = the cost of debt financing for each firm (%),

Rf = the expected return on risk-free assets (%), and 

Ra = the risk adjustment factor to risk-free rate for each firm (%). 

8-28
The risk adjustment factor depends on the variability of stock returns represented by 

standard deviations in stock prices. DOE calculated stock prices using values from Damodaran 

Online individual company cost of capital worksheets.11

The weighted average cost of capital (WACC) for a company is the weighted average 

cost of debt and equity financing:

k =ke x we+ kd x wd

Eq. 8.9

Where:

k = the (nominal) cost of capital (%),

ke = the expected rate of return on equity (%),

kd = the expected rate of return on debt (%),

we = the proportion of equity financing in total annual financing, and

wd = the proportion of debt financing in total annual financing.

The cost of capital is a nominal rate, because it includes anticipated future inflation in the

expected returns from stocks and bonds. The real discount rate or WACC deducts expected 

inflation (r) from the nominal rate. DOE calculated expected inflation (3.42 percent) over the

same 1974 through 2013 historical period used for the other financial data calculations.

To estimate the WACC of automatic commercial ice maker purchasers, DOE used a

dataset of companies involved in each of the building types being analyzed, drawn from a 

database of U.S. companies given on the Damodaran Online individual company worksheet cited 

earlier. The Damodaran database includes most of the publicly traded companies in the United 

States.

DOE divided the companies into categories according to their type of activity (e.g., 

foodservice or food sales). DOE used financial information for firms in the Damodaran database 

that would be likely to occupy one of seven building types that DOE assumed likeliest to use 

automatic commercial ice makers. DOE used all observations from the Damodaran data set for 

which sufficiently complete data were available.

Table 8.2.20 outlines the building type and ownership categories as well as the number of 

companies used for determining discount rates. For five of the seven building types, there is a 

mixture of large companies with stock traded on major U.S. stock exchanges, and smaller 

companies that are not publicly traded—e.g., single-store or small, local chains of convenience 

stores or restaurants—and/or state and local government facilities. The cost of capital for small, 

independent grocers; convenience store franchisees; gasoline station owner-operators; small 

hotel owner-operators; and others with more limited access to capital is more difficult to 

determine than for publicly traded companies. Individual credit worthiness varies considerably, 

and some franchisees have access to the financial resources of the franchising corporation. The 

Small Business Administration has collected data from which DOE was able to estimate the 

premium small businesses pay on loans relative to AAA corporate bonds. For small business 

8-29
loans under $100,000, the premium was approximately 1.9 percent for fixed rate loans.

12 This 

assumption was applied in the LCC modeling.

Table 8.2.20 Derivation of Typical Discount Rates by Building Type

Building 

Type

Chain/Publicly 

Traded

Local/NonTradedState & Local 

Government US Government Weighted 

Average, 

Real

No.

of

WACC Obs. % of 

Mkt

Rate

With

Adder7

% of 

Mkt

Municip.

Bond 

Rate

% of 

Mkt

Treasury 

Rate

% of 

Mkt

Health Care 

1,2 6.64% 19.6% 0% 0% 2.57% 76.7% 2.73% 3.7% 3.37% 18

Lodging 3 7.08% 60% 1.90% 40% 0.00% 0.0% 0.0% 0.0% 7.90% 28

Food 

Service 4 6.30% 26% 1.90% 61% 2.57% 12.5% 0.0% 0.0% 7.07% 52

Retail 5.80% 100% 0.0% 0% 0.0% 0.0% 0.0% 0.0% 5.80% 8

Education 5 7.79% 26% 0.0% 0% 2.57% 73.5% 0.0% 0.0% 3.95% 16

Food Sales 5.88% 50% 1.90% 50% 0.0% 0.0% 0.0% 0.0% 6.88% 24

Office6 6.74% 86% 0.0% 0% 2.57% 10.0% 2.73% 4.0% 6.16% 1318 1 Percentage of market from American Hospital Association, Fast Facts on Hospitals, Chicago, IL, January 3, 2012. 

www.aha.org/. Percentages based on number of community hospitals. 2 Municipal bond rate from Federal Reserve Bank of St. Louis, and Federal Reserve H15 data (for 2012 and 2013 only). 

Value shown in the table is 40-year geometric average, mixed quality bonds, based on annual average data. Non-seasonally 

adjusted. Data for 1973–2011 from the Federal Reserve Bank of St. Louis, and 2012–2013 from the H15 data. 3 Press reports indicate the percentage of “branded” hotels ranges from 60 to 70 percent of total hotel rooms. Articles did not 

indicate the percentage of branded hotel rooms that are franchisees essentially indistinguishable from local, small

businesses. The low end of the range was used for the percentage of the market.

4 Percentages of market developed represented by state & local government developed from U.S. Census Bureau data, Table 

1283. Commercial and Noncommercial Groups - Food and Drink. Establishments and Sales: 1990 to 2011, U.S. Census 

Bureau, Statistical Abstract of the United States: 2012. Publicly traded and small businesses subject to the small business 

discount adder estimated using a National Restaurant Association comment that 7 of 10 restaurants are single 

establishments.

5 Source for the percentage of education that is public versus private: 

http://nces.ed.gov/programs/digest/d10/tables/dt10_090.asp. 6 Source for the fraction of offices owned by state and local government, and by federal government, was the 2003 CBECS.

7 Source: Small Business Administration data on loans between $10,000 and $99,000 compared to AAA Corporate Rates. 

www.sba.gov/advocacy/7540/6282. Last accessed June 10, 2013.

For buildings with an education application, the Damodaran database covers some 

privately operated schools, but not public schools. For the education sector, DOE obtained a 

40-year series of municipal bond rates from the Federal Reserve.

13 As shown in the discount rate 

calculation, DOE assumed 75 percent of schools are public schools facing the average municipal 

bond rates calculated from the Federal Reserve data. The same municipal bond rate was used in 

the calculation of office and foodservice facilities owned by state and local governments. The 

long-term, real Treasury bond rates were used for the cost of capital of U.S. government-owned 

offices.

8.2.3.9 Compliance Date of Standard

The compliance date is the future date when a new standard will become operative (i.e., 

the date on which manufacturers must be compliant with the new DOE standard). Under 

42 U.S.C. 6313(d)(2)(B), the compliance date of any new energy conservation standard for

automatic commercial ice makers must be no less than 3 years after the final rule is published, 

and may be up to 5 years after publication. DOE calculated the LCC for all customers as if they 

8-30
each would purchase a new automatic commercial ice maker in the year the standard takes effect.

DOE has set the new standards to take effect in 2018. For the LCC analysis, the year of 

equipment purchase is 2018. However, all dollar values are expressed in 2013$.

8.3 PAYBACK PERIOD INPUTS

8.3.1 Definition

As stated in section 8.1, the PBP is the amount of time it takes the customer to recover 

the higher purchase price of more energy efficient equipment through lower operating costs.

Numerically, the PBP is the ratio of the increase in purchase cost to the decrease in annual 

operating expenditures. This type of calculation is known as a “simple” payback period because 

it does not take into account changes in operating cost over time or the time value of money, that 

is, the calculation is done at an effective discount rate of zero percent.

The equation for PBP is:

PBP =∆IC/∆OC

Eq. 8.10

Where:

PBP = payback period in years,

∆IC = difference in the total installed cost between the more efficient standard level and the 

baseline equipment, and

∆OC = difference in annual (first year) operating costs.

PBPs greater than the life of the product mean that the increased total installed cost of the 

more energy efficient equipment is not recovered in reduced operating costs over the life of the 

equipment, even when no discount rate is applied.

8.3.2 Inputs

The data inputs to PBP are the total installed cost of the equipment to the customer for 

each efficiency level and the annual (first year) operating costs for each efficiency level. The 

inputs to the total installed cost are the customer’s equipment price and the installation cost. The 

inputs to the operating costs are the annual energy and water costs, the annual repair cost, and the 

annual maintenance cost. The PBP calculation uses the same inputs as the LCC analysis 

described in section 8.2, except that electricity and water price trends and discount rates are not 

required because the PBP is a “simple” (undiscounted) payback and the required electricity and 

water prices are only for the year in which a new efficiency standard is to take effect—in this 

case, the year 2018. The electricity and water prices used in the PBP calculation were the prices

projected for 2018, expressed in 2013$. Discount rates are not used in the PBP calculation.

8.4 LIFE-CYCLE COST AND PAYBACK PERIOD RESULTS

The results of the LCC and PBP analysis are presented in this section. Mean values of 

LCC savings and PBP are presented along with a summary of the distribution of these values.

8-31
8.4.1 Life-Cycle Cost Results

Figure 8.4.1 shows the change in LCC over the seven efficiency levels for the IMH-ASmall-B equipment class. The LCC values on this chart are mean values obtained from the LCC 

analysis. This curve is presented here as an example to illustrate the typical relationship between 

installation cost and LCC values over all the efficiency levels in an equipment class. The 

installed costs increase steadily from the baseline to the highest possible efficiency level and the 

LCCs decrease from level 1 to the highest possible efficiency level. 

Figure 8.4.1 LCC and Installed Cost Variation over Efficiency Levels for IMH-A-Small-B

Equipment Class

As stated previously, DOE calculated the LCC savings for a range of building types, 

discount rates, and geographic locations. Figure 8.4.2 illustrates an example of the ranges of 

LCC savings for the IMH-A-Small-B equipment class. Appendix 8B includes similar plots of 

LCC savings for all equipment classes analyzed. Table 8.4.1 presents the numerical values 

associated with Figure 8.4.2. Figure 8.4.2 illustrates the mean and median values on the plot 

using red and blue markers, respectively. The elongated, large rectangular box represents the 25th

and 75th percentile values. The lower edge of the elongated rectangle represents 25th percentile, 

which means that 25 percent of the customers would experience LCC savings of $39 or less if 

the standard were set at level 2, ($24) or less in LCC savings if the standard were set at level 3, 

and so on. The median value of LCC savings is equal to the 50th percentile. The upper edge of 

the elongated rectangle represents the 75th percentile. The two ends of the vertical black line for 

each efficiency level represent the 5th percentile (lower end) and 95th percentile (upper end).

8-32
Figure 8.4.2 LCC Savings Ranges by Efficiency Level for Equipment Class IMH-ASmall-B

Table 8.4.1 LCC Savings Distribution Results for Equipment Class IMH-A-Small-B LCC Savings 201

3

$

Efficiency 

Level Level 1 Level 2 Level 3 Level 3A Level 4 Level 5 Level 6

Mean $136 $72 $77 -$68 -$400 -$393

Median (50th 

Percentile) $99 $21 $34 -$112 -$457 -$452

5th Percentile -$2 -$58 -$49 -$207 -$607 -$607

25th Percentile $39 -$25 $5 -$149 -$525 -$523

75th Percentile $196 $109 $95 -$42 -$335 -$324

95th Percentile $401 $396 $363 $226 -$7 $11

Table 8.4.2 and Table 8.4.3 summarize the mean and median LCC savings, respectively,

for all equipment classes analyzed.

-$800

-$600

-$400

-$200

$0

$200

$400

$600

2 3 3a 4 5 6

Cost Savings (2013$)

Efficiency Level

Life-Cycle Cost Savings

Mean Median

8-33
Table 8.4.2 Mean LCC Savings for All Equipment Classes and Efficiency Levels

Equipment Class

Mean Life-Cycle Cost Savings 

2013$*

Level

2

Level

3

Level 

3A

Level

4

Level

4A

Level

5

Level

6

Level

7

IMH-W-Small-B 175 214 (148) (333) (534)

IMH-W-Med-B 308 165 (63)

IMH-W-Large-B** (172)

 IMH-W-Large-B1 (200)

 IMH-W-Large-B2 (80)

IMH-A-Small-B 136 72 77 (68) (400) (393)

IMH-A-Large-B** 382 507 361 265 55 

 IMH-A-Large-B1 439 580 407 294 45 

 IMH-A-Large-B2 76 110 

RCU-Large-B** 748 360 144 

 RCU-Large-B1 743 391 161 

 RCU-Large-B2 820 (109) 

SCU-W-Large-B 444 527 613 550 192 

SCU-A-Small-B 110 134 161 281 230 (145)

SCU-A-Large-B 163 206 400 439 71 

IMH-A-Small-C 245 292 313 (207) (165) 

IMH-A-Large-C 539 626 (10) 28 

RCU-Small-C 498 448 505 (41) (73) 

SCU-A-Small-C 224 278 290 (291) (268) 

* Values on this table represent LCC savings for customers affected by the standard. Blank cells mean no LCC savings 

were calculated for this efficiency level because design options were unavailable to constitute an additional efficiency level. 

Values in parenthesis indicate negative numbers. Levels 3A and 4A are only applicable to classes with 22-inch and non-22-

inch cost efficiency curves.

** Results for IMH-W-Large-B, IMH-A-Large-B, and RCU-Large-B are weighted averages of the two sub-equipment class 

level typical units shown in the table, using the weights provided in TSD chapter 7.

8-34
Table 8.4.3 Median LCC Savings for All Equipment Classes and Efficiency Levels

Equipment Class

Median Life-Cycle Cost Savings 

2013$*

Level

2

Level

3

Level

3A

Level

4

Level

4A

Level

5

Level

6

Level

7

IMH-W-Small-B 140 155 (221) (405) (603)

IMH-W-Med-B 248 73 (173)

IMH-W-Large-B** (259)

 IMH-W-Large-B1 (277)

 IMH-W-Large-B2 (200)

IMH-A-Small-B 99 21 34 (112) (457) (452)

IMH-A-Large-B** 297 387 238 115 (105)

 IMH-A-Large-B1 361 449 272 127 (134)

 IMH-A-Large-B2 (50) 52 

RCU-Large-B** 624 166 (87)

 RCU-Large-B1 624 204 (66) 

 RCU-Large-B2 628 (400) 

SCU-W-Large-B 377 444 511 446 59 

SCU-A-Small-B 89 101 117 216 152 (229)

SCU-A-Large-B 133 162 322 347 (30) 

IMH-A-Small-C 200 222 216 (317) (208) 

IMH-A-Large-C 454 495 (172) (83) 

RCU-Small-C 418 321 356 (205) (247) 

SCU-A-Small-C 189 229 220 (381) (343) 

* Values on this table represent LCC savings for customers affected by the standard. Blank cells mean no LCC savings 

were calculated for this efficiency level because design options were unavailable to constitute an additional efficiency level. 

Values in parenthesis indicate negative numbers. Levels 3A and 4A are only applicable to classes with 22-inch and non-22-

inch cost efficiency curves.

** Results for IMH-W-Large-B, IMH-A-Large-B, and RCU-Large-B are weighted averages of the two sub-equipment class 

level typical units shown in the table, using the weights provided in TSD chapter 7.

8.4.2 Payback Period Results

Figure 8.4.3 presents the distribution of the PBP results for efficiency levels above the 

baseline for the equipment class IMH-A-Small-B. Table 8.4.4 presents the numerical values 

associated with this plot. The red marker represents the mean and the blue marker represents the 

median PBP for each efficiency level. The lower edge of the elongated rectangular box 

represents the 25th percentile, which means that 25 percent of the customers would experience a 

PBP of 2.3 years or less if the energy conservation standard were set at level 2, 3.2 years or less

if the energy conservation standard were set at level 3, and so on. The upper edge of the 

rectangular box represents the 75th percentile. The two ends of the vertical line represent the 5th

percentile (lower end) and 95th percentile (upper end). Table 8.4.4 and Table 8.4.5 summarize the 

mean and median PBPs, respectively, for all efficiency levels for all the analyzed equipment 

classes.

8-35
Figure 8.4.3 Payback Period for All Efficiency Levels for the Equipment Class IMH-ASmall-B

Table 8.4.4 Payback Period Distribution Results for IMH-A-Small-B Payback Period years

Efficiency Level

Level

1

Level

2

Level

3

Level

3A

Level

4

Level

5

Level

6

Mean - 3.6 5.0 4.9 7.7 12.5 12.3 

Median (50th 

Percentile) - 3.4 4.8 4.7 7.3 12.1 11.9 

5th Percentile - 1.4 2.0 1.9 3.1 5.0 5.0 

25th Percentile - 2.3 3.2 3.1 5.0 8.2 8.1 

75th Percentile - 4.7 6.6 6.4 10.0 16.3 16.0 

95th Percentile - 6.6 9.0 8.6 13.8 22.2 21.8 

0.00

5.00

10.00

15.00

20.00

25.00

2 3 3a 4 5 6

Years

Efficiency Level

Payback Period 

Median Mean

8-36
Table 8.4.5 Median Payback Period for All Equipment Classes and Efficiency Levels

Equipment Class

Median payback period

years*,**

Level 

2

Level 

3

Level 

3A

Level 

4

Level 

4A

Level 

5

Level 

6

Level 

7

IMH-W-Small-B 2.5 2.7 8.1 10.9 13.4

IMH-W-Med-B 2.1 5.0 7.6

IMH-W-Large-B** 10.6

 IMH-W-Large-B1 11.1

 IMH-W-Large-B2 8.9

IMH-A-Small-B 3.4 4.8 4.7 7.3 12.1 11.9

IMH-A-Large-B** 2.2 2.3 2.3 3.9 5.6

 IMH-A-Large-B1 1.2 1.5 1.5 3.4 5.4

 IMH-A-Large-B2 7.4 6.9

RCU-Large-B** 1.1 3.6 5.0

 RCU-Large-B1 0.9 3.4 4.9

 RCU-Large-B2 3.0 7.0

SCU-W-Large-B 1.1 1.4 1.6 1.8 5.1

SCU-A-Small-B 2.2 2.3 2.4 2.6 3.5 8.9

SCU-A-Large-B 1.8 1.9 1.6 2.1 6.5

IMH-A-Small-C 1.5 1.6 1.7 8.7 8.8

IMH-A-Large-C 0.7 0.7 5.9 5.9

RCU-Small-C 0.7 1.2 1.2 5.5 5.8

SCU-A-Small-C 0.8 1.1 1.5 10.7 11.4

* Blank cells mean design options were unavailable to constitute an additional efficiency level. Levels 3A and 

4A are only applicable to classes with 22-inch and non-22-inch cost efficiency curves.

** Results for IMH-W-Large-B, IMH-A-Large-B, and RCU-Large-B are weighted averages of the two subequipment class level typical units shown in the table, using the weights provided in TSD chapter 7.

8.4.3 Rebuttable Presumption Payback Period

EPCA establishes a rebuttable presumption for automatic commercial ice-making

equipment. (42 U.S.C. 6295(o)(2)(B)(iii) and 6313(d)(4)) The rebuttable presumption states that 

a standard is economically justified if the Secretary of Energy finds that the additional cost to the 

customer of purchasing a product complying with an energy conservation standard level will be 

less than three times the value of the energy savings during the first year that the customer will 

receive as a result of the standard, as calculated under the applicable test procedure. This 

rebuttable presumption test is an alternative path to establishing economic justification.

To evaluate the rebuttable presumption, DOE determined the additional cost of 

purchasing more-efficient, standards-compliant equipment, and compared this cost to the value 

of the energy saved during the first year of operation of the equipment. DOE interprets that the 

increased cost of purchasing standards-compliant equipment includes the cost of installing the 

equipment for use by the purchaser. DOE calculated the rebuttable presumption payback period 

(RPBP), or the ratio of (a) the increase in installed cost above the baseline efficiency level to (b) 

the first year energy cost savings. When RPBP is less than 3 years, the rebuttable presumption is 

satisfied; when RPBP is equal to or more than 3 years, the rebuttable presumption is not 

satisfied. This payback period calculation does not include other components of the annual 

operating cost of the equipment (i.e., water costs, maintenance costs, and repair costs). The 

RPBPs calculated can thus be different from the PBPs calculated in section 8.4.2.

8-37
DOE calculated the RPBPs for the range of installed costs and energy prices discussed in 

sections 8.4.1 and 8.4.2, which are representative of the same seven building types and all 50 

states plus the District of Columbia. DOE calculated the RPBP for each higher efficiency level 

within each equipment class.

Table 8.4.6 shows the RPBPs calculated for all equipment classes and efficiency levels.

Table 8.4.6 Rebuttable Presumption Payback Periods by Efficiency Level and Equipment

Class

Equipment Class

Rebuttable Payback Period 

years*,**

Level 

2

Level 

3

Level 

3A

Level 

4

Level 

4A

Level 

5

Level 

6

Level 

7

IMH-W-Small-B 2.9 3.4 9.5 12.7 15.4 

IMH-W-Med-B 1.9 4.6 7.1 

IMH-W-Large-B** 9.9 

 IMH-W-Large-B1 10.4 

 IMH-W-Large-B2 8.3 

IMH-A-Small-B 3.0 4.3 4.2 6.4 10.7 10.6 

IMH-A-Large-B** 2.0 2.4 2.4 4.1 5.8 

 IMH-A-Large-B1 1.1 1.7 1.6 3.7 5.8 

 IMH-A-Large-B2 6.7 6.3 

RCU-Large-B** 1.0 3.3 4.7 

 RCU-Large-B1 0.9 3.1 4.6 

 RCU-Large-B2 2.7 6.2 

SCU-W-Large-B 2.2 2.3 2.3 2.4 6.7 

SCU-A-Small-B 2.0 2.1 2.1 3.1 4.0 9.9 

SCU-A-Large-B 1.7 1.7 2.1 2.5 7.7 

IMH-A-Small-C 1.3 1.5 1.6 6.4 6.2 

IMH-A-Large-C 0.6 0.7 5.2 4.5 

RCU-Small-C 0.7 1.1 1.2 4.7 4.6 

SCU-A-Small-C 0.7 1.0 1.4 7.8 7.4 

* Blank cells mean design options were unavailable to constitute an additional efficiency level. Levels 3A 

and 4A are only applicable to classes with 22-inch and non-22-inch cost efficiency curves.

** Results for IMH-W-Large-B, IMH-A-Large-B, and RCU-Large-B are weighted averages of the two 

sub-equipment class level typical units shown in the table, using the weights provided in TSD Chapter 7.

8.5 DETAILED RESULTS

Appendix 8B presents detailed results from the LCC analysis. Plots similar to 

Figure 8.4.2 and Figure 8.4.3 are presented in the appendix for all equipment classes. In addition, 

summary tables with all the necessary data in one table for each equipment class are presented. 

Table 8.5.1 is the summary table for IMH-A-Small-B equipment class. This table presents the 

mean values of installed costs, annual operating costs, LCC, LCC savings, and median PBP 

values for all the efficiency levels. It also presents the percentage of customers who experience 

net cost, no impact, and net benefit. The average LCC savings and the percentage of customers 

experiencing a net benefit or cost are based on a range of efficiency choices. In the base case, 

DOE does not assume that all customers buy equipment at the baseline efficiency (level 1). As 

discussed in chapter 10, DOE assumes that some are buying at higher efficiency levels. The LCC 

savings is an average of the savings achieved by customers who, in the base case, were buying 

less-efficient equipment than the efficiency level examined. DOE assumed that customers with 

8-38
no impact in the base case are already buying more-efficient equipment, so the efficiency level in 

question would not affect those customers.

Table 8.5.1 Summary of Results of LCC and PBP Analysis for IMH-A-Small-B Equipment 

Class

Efficiency 

Level 

Number

Efficiency 

Level 

kWh/yr

Life-Cycle Cost, All Customers Life-Cycle Cost Savings

Payback 

Period, 

Median 

years

Installed

Cost

2013$

Total

Discounted

Operating

Cost

2013$

LCC,

All 

Customers

2013$

Affected

Customers’

Average

Savings

2013$

% of Customers that 

Experience

Net

Cost

%

No 

Impact

%

Net

Benefit

%

1 3,535 2,442 8,654 11,096 0 NA NA NA NA

2 3,184 2,539 8,420 10,959 136 1 76 22 3.4

3 3,009 2,655 8,293 10,948 72 21 47 32 4.8

3A 2,901 2,695 8,214 10,909 77 21 0 79 4.7

4 2,833 2,865 8,189 11,054 -68 82 0 18 7.3

5 2,658 3,324 8,062 11,386 -400 95 0 5 12.1

6 2,640 3,331 8,048 11,379 -393 95 0 5 11.9

8-39
REFERENCES

1. U. S. Department of Energy—Energy Information Administration. Annual Energy Outlook 

2014. 2014. Washington D.C. DOE/EIA-0383(2014). (Last accessed November 19, 2014.) 

<http://www.eia.gov/oiaf/aeo/tablebrowser/>

2. American Water Works Association. 2012 Water and Wastewater Rate Survey. Rep. no. 

54008. 2013. See also the 2010 Water and Wastewater Rate Survey and 2008 Water and 

Wastewater Rate Survey, published in 2011 and 2009, respectively.

3. RS Means Company, Inc. 2014 RS Means Construction Cost Data. 2014. Kingston, MA.

4. RS Means 2014. City Cost Indexes, RJ1030-010 Building Systems. RS Means 

Construction Publishers & Consultants, Kingston, MA.

5. U.S. Census Bureau, Population Division. Annual Estimates of the Resident Population of 

Incorporated Places: April 1, 2010 to July 1, 2013. May 2014. (Last accessed June 24, 

2014.) <www.census.gov/popest/data/cities/totals/2013/index.html>

6. U.S. Census Bureau. Annual Estimates of the Population for the United States, Regions, 

States, and Puerto Rico: April 1, 2010 to July 1, 2013. (NST-EST2013-01). December 

2013. (Last accessed May 13, 2014.) 

<www.census.gov/popest/data/state/totals/2013/index.html> 

7. U.S. Department of Energy–Energy Information Administration. Form EIA-826 data 

from the EIA web page Sales (Consumption), Revenue, Prices and Customers. (Last 

accessed May 19, 2014.) < http://www.eia.gov/electricity/data.cfm#sales>

8. U.S. Department of Energy–Energy Information Administration. 2003 CBECS Survey 

Data. (Last accessed October 28, 2013.) 

<www.eia.gov/consumption/commercial/data/2003/>

9. Harris, R.S. Applying the Capital Asset Pricing Model. UVA-F-1456. Available at 

SSRN: <http://ssrn.com/abstract=909893>.

10. U.S. Federal Reserve. H.15: Selected Interest Rates. (Last accessed May 21, 2014.) 

<www.federalreserve.gov/releases/h15/data.htm>

11. Damodoran Online. The Data Page. 2014. (Last accessed June 24, 2014.) 

<http://pages.stern.nyu.edu/~adamodar/New_Home_Page/data.html>

12. U.S. Small Business Administration, Office of Advocacy. The Small Business Economy 

2012. 2012. (Last accessed June 4, 2013.) <http://www.sba.gov/advocacy/7540/6282>. See 

also: U.S. Small Business Administration, Office of Advocacy. The Small Business 

Economy 2009. 2009. <www.sba.gov/advocacy/7540/6282> 

8-40
13. Federal Reserve Bank of St. Louis (http://research.stlouisfed.org/fred2/) and Federal 

Reserve H15 data (www.federalreserve.gov/releases/h15/data.htm). 

8-41
APPENDIX 8A. USER INSTRUCTIONS FOR LIFE-CYCLE COST SPREADSHEET

TABLE OF CONTENTS

8A.1 INTRODUCTION ........................................................................................................ 8A-1

8A.2 USER INSTRUCTIONS FOR LIFE-CYCLE COST SPREADSHEET...................... 8A-1

REFERENCES ......................................................................................................................... 8A-4

LIST OF TABLES

Table 8A.2.1 Description of Worksheets in LCC Spreadsheet ................................................ 8A-1

8A-i
APPENDIX 8A. USER INSTRUCTIONS FOR LIFE-CYCLE COST SPREADSHEET

8A.1 INTRODUCTION

Detailed results of the life-cycle cost (LCC) and payback period analyses for automatic 

commercial ice-making (ACIM) equipment can be examined using a Microsoft Excel 

spreadsheet available on the U.S. Department of Energy’s (DOE’s) Building Technologies (BT)

website at www1.eere.energy.gov/buildings/appliance_standards/rulemaking.aspx/ruleid/29.

8A.2 USER INSTRUCTIONS FOR LIFE-CYCLE COST SPREADSHEET

The spreadsheet allows the user to perform LCC analyses of any of the primary

equipment classes of ACIM equipment. To fully execute the spreadsheet requires Microsoft 

Excel and Oracle’s Crystal Ball software, both of which are commercially available.

The spreadsheet posted on the DOE website represents the latest version of the applicable 

model and has been developed and tested with Microsoft Excel 2010 and with Oracle Crystal 

Ball, Fusion Edition, Release 11.1.2.1.000. Table 8A.2.1 describes the worksheets in the LCC 

spreadsheet.

Table 8A.2.1 Description of Worksheets in LCC Spreadsheet

Worksheet Description

LCC Summary Contains the input selections and a summary table of energy and water use, operating costs, 

LCC, and payback.

Labels This is used as a go-between for user inputs and the rest of the worksheets. Modifying this 

worksheet can change and possibly impair operation of the model, so DOE recommends 

caution when editing this worksheet.

Crystal Ball Contains outputs for each equipment class from the Crystal Ball/Monte Carlo analysis. 

NIA Output Contains a full output of all data that is uploaded to the National Impacts Analysis (NIA)

Model. 

Econ Trends Summarizes electricity and water price forecasts for use in the NIA model. Also contains

experiential (price) learning data inputs used in the LCC and NIA model.

Markups Contains data used to derive industry baseline and incremental markups.

Engineering Contains the manufacturer price data for units at each efficiency level. Also contains data on 

harvest rates, utilization rates, and energy and water consumption, and summarizes other 

engineering derived data.

Discounted Costs Contains annual operating costs in cash flow tables. 

Discount Rate Contains all data used to calculate discounts rates for each building type.

Electricity Prices Contains electricity prices by state, Annual Energy Outlook 2014 (AEO2014)

1 electricity price 

projections, and electricity price ratios derived for the various building types using 2003 

Commercial Building Energy Consumption (CBECS) data.

Water Prices Contains population-weighted water/wastewater prices by state, and water/wastewater price 

projections.

Installation Costs Contains adjustment factors to vary installation costs by state. Derived from data published by 

RS Means Publishers and Consultants and U.S. Census Bureau population estimates.

Sales Tax Contains data on sales tax rates and population data by state.

Population Contains 2013 census data on population by state.

8A-1
Basic instructions for operating the LCC spreadsheets are as follows:

1) Once you have downloaded the LCC file from the DOE BT website, open the file using 

Excel.

a) Select Yes when asked if you want to enable macros.

b) At the bottom of the Excel window, click the tab for the Summary worksheet. Note 

that if you plan to run the Monte Carlo routine, you will need to have Crystal Ball 

loaded as an add-in and activated. The model is structured to run in the Monte Carlo 

mode.

2) Use Excel’s View/Zoom commands at the top menu bar to size the display to your 

monitor.

3) You can interact with the spreadsheet by clicking choices or entering data using the 

graphical interface on the LCC Summary worksheet. Select choices from the various 

drop-down input boxes throughout the LCC Summary worksheet.

4) From the user-defined input options, select from the buttons and boxes for the following:

(1) assumed baseline level, (2) equipment class, (3) analysis year (year of purchase),

(4) economic growth case, (5) year to discount costs to, (6) standard efficiency level,

(7) building type, (8) state or region to analyze, (9) conservation program to model,

(10) installation cost escalation, (11) repair cost escalation, (12) analysis type, (13) price 

learning toggle, and (14) discounting toggle. 

a) You can start a complete Crystal Ball simulation by clicking the “Monte Carlo”

button.

b) All inputs, with the exception of some conservation program inputs, are either dropdown option boxes or toggle boxes (checked boxes make the statement true).

c) Equipment classes are defined as particular combinations of equipment families, 

cooling methods, harvest rate bins, and ice-making processes. Because there are three

potential equipment families, two potential cooling methods (air and water), two or 

three possible harvest rate bins, and two different ice-making processes, there are 21

potential equipment classes that could be analyzed.a However, during the final rule

analyses, 13 primary equipment classes were studied, including 3 classes that were 

modeled at the level of two typical unit sizes. If you select an equipment class that 

has not been analyzed by DOE, the model will display zeros and/or blank cells in the 

inputs and results summary on the LCC_Summary tab.

a Remote condensing units can either have remote compressors or not. Thus, there are 8 possible RCU equipment 

classes, and these have been condensed down to 4 classes.

8A-2

 
d) There is room for up to eight possible efficiency levels, including the baseline 

efficiency level, though no equipment classes have more than seven representative 

levels.

5) This spreadsheet gives the user two types of calculation methods:

a) If base and sensitivity analysis is chosen, then all calculations are performed for a set 

of single input values. The new results are shown on the Summary tab as soon as the 

input values are changed through a drop-down option box or toggle box. Note that the

model was set up to run in Monte Carlo mode with a distribution of equipment 

lifetimes that are integer values. In base and sensitivity mode the model also runs

equipment lifetimes that are integers.

b) When the Monte Carlo option is chosen and the “Monte Carlo” button is clicked, the 

spreadsheet generates a set of results from calculations for each equipment class with 

populated engineering data sets. 

• For a number of inputs, the Crystal Ball software has custom distributions that it 

uses to set the level of the input. The model runs each of the studied classes in 

turn, performing 10,000 model runs for each. At the end of each equipment class 

run, the model records output to the Crystal Ball tab for further analysis.

• The “Monte Carlo” button executes a macro written in Visual Basic for 

Applications to loop through all equipment classes and selected efficiency levels. 

The Monte Carlo analysis usually takes 2 to 3 hours to complete for all equipment 

classes, depending on computer speed and memory.

8A-3
REFERENCES

1. U.S. Department of Energy–Energy Information Administration. Annual Energy Outlook 

2014. 2014. Washington D.C. DOE/EIA-0383(2014).

8A-4
APPENDIX 8B. DETAILED LIFE-CYCLE COST AND PAYBACK PERIOD 

ANALYSIS RESULTS

TABLE OF CONTENTS

8B.1 INTRODUCTION .........................................................................................................8B-1

8B.2 LIFE CYCLE COST RESULTS ...................................................................................8B-2

8B.2.1 Life-Cycle Cost and Payback Period Results for Ice-Making Head, WaterCooled, Small, Batch (IMH-W-Small-B)..........................................................8B-2

8B.2.2 Life-Cycle Cost and Payback Period Results for Ice-Making Head, WaterCooled, Medium, Batch (IMH-W-Med-B)........................................................8B-4

8B.2.3 Life-Cycle Cost and Payback Period Results for Ice-Making Head, WaterCooled, Large, Batch (smaller, 1,500 lb unit) (IMH-W-Large-B1) ..................8B-6

8B.2.4 Life-Cycle Cost and Payback Period Results for Ice-Making Head, WaterCooled, Large, Batch (larger, 2,600 lb unit) (IMH-W-Large-B2).....................8B-8

8B.2.5 Life-Cycle Cost and Payback Period Results for Ice-Making Head, AirCooled, Small, Batch (IMH-A-Small-B).........................................................8B-10

8B.2.6 Life-Cycle Cost and Payback Period Results for Ice-Making Head, AirCooled, Large, Batch (smaller, 800 lb unit) (IMH-A-Large-B1) ....................8B-12

8B.2.7 Life-Cycle Cost and Payback Period Results for Ice-Making Head, AirCooled, Large, Batch (larger, 1,500 lb unit) (IMH-A-Large-B2)....................8B-14

8B.2.8 Life-Cycle Cost and Payback Period Results for Remote Condensing Unit, 

Large, Batch (smaller, 1,500 lb unit) (RCU-Large-B1)...................................8B-16

8B.2.9 Life-Cycle Cost and Payback Period Results for Remote Condensing Unit, 

Large, Batch (larger, 2,400 lb unit) (RCU-Large-B2).....................................8B-18

8B.2.10 Life-Cycle Cost and Payback Period Results for Self-Contained Unit, WaterCooled, Large, Batch (SCU-W-Large-B)........................................................8B-20

8B.2.11 Life-Cycle Cost and Payback Period Results for Self-Contained Unit, AirCooled, Small, Batch (SCU-A-Small-B).........................................................8B-22

8B.2.12 Life-Cycle Cost and Payback Period Results for Self-Contained Unit, AirCooled, Large, Batch (SCU-A-Large-B).........................................................8B-24

8B.2.13 Life-Cycle Cost and Payback Period Results for Ice-Making Head, AirCooled, Small, Continuous (IMH-A-Small-C)................................................8B-26

8B.2.14 Life-Cycle Cost and Payback Period Results for Ice-Making Head, AirCooled, Large, Continuous (Smallest, 820 lb unit) (IMH-A-Large-C)...........8B-28

8B.2.15 Life-Cycle Cost and Payback Period Results for Remote Condensing Unit, 

Small, Continuous (RCU-Small-C).................................................................8B-30

8B.2.16 Life-Cycle Cost and Payback Period Results for Self-Contained Unit, AirCooled, Small, Continuous (SCU-A-Small-C)................................................8B-32

LIST OF TABLES

Table 8B.2.1 LCC and PBP Results for Ice-Making Head, Water-Cooled, Small, Batch 

(IMH-W-Small-B) .............................................................................................8B-2

8B-i
Table 8B.2.2 LCC and PBP Results for Ice-Making Head, Water-Cooled, Medium, Batch 

(IMH-W-Med-B) ...............................................................................................8B-4

Table 8B.2.3 LCC and PBP Results for Ice-Making Head, Water-Cooled, Large, Batch 

(smaller, 1,500 lb unit) (IMH-W-Large-B1)......................................................8B-6

Table 8B.2.4 LCC and PBP Results for Ice-Making Head, Water-Cooled, Large, Batch 

(larger, 2,600 lb unit) (IMH-W-Large-B2)........................................................8B-8

Table 8B.2.5 LCC and PBP Results for Ice-Making Head, Air-Cooled, Small, Batch (IMHA-Small-B).......................................................................................................8B-10

Table 8B.2.6 LCC and PBP Results for Ice-Making Head, Air-Cooled, Large, Batch (smaller, 

800 lb unit) (IMH-A-Large-B1).......................................................................8B-12

Table 8B.2.7 LCC and PBP Results for Ice-Making Head, Air-Cooled, Large, Batch (larger, 

1,500 lb unit) (IMH-A-Large-B2)....................................................................8B-14

Table 8B.2.8 LCC and PBP Results for Remote Condensing Unit, Large, Batch (smaller, 

1,500 lb unit) (RCU-Large-B1) .......................................................................8B-16

Table 8B.2.9 LCC and PBP Results for Remote Condensing Unit, Large, Batch (larger, 2,400 

lb unit) (RCU-Large-B2) .................................................................................8B-18

Table 8B.2.10 LCC and PBP Results for Self-Contained Unit, Water-Cooled, Large, Batch 

(SCU-W-Large-B) ...........................................................................................8B-20

Table 8B.2.11 LCC and PBP Results for Self-Contained Unit, Air-Cooled, Small, Batch 

(SCU-A-Small-B) ............................................................................................8B-22

Table 8B.2.12 LCC and PBP Results for Self-Contained Unit, Air-Cooled, Large, Batch 

(SCU-A-Large-B) ............................................................................................8B-24

Table 8B.2.13 LCC and PBP Results for Ice-Making Head, Air-Cooled, Small, Continuous 

(IMH-A-Small-C) ............................................................................................8B-26

Table 8B.2.14 LCC and PBP Results for Ice-Making Head, Air-Cooled, Large, Continuous 

(Smallest, 820 lb unit) (IMH-A-Large-C) .......................................................8B-28

Table 8B.2.15 LCC and PBP Results for Remote Condensing Unit, Small, Continuous 

(RCU-Small-C)................................................................................................8B-30

Table 8B.2.16 LCC and PBP Results for Self-Contained Unit, Air-Cooled, Small, 

Continuous (SCU-A-Small-C).........................................................................8B-32

LIST OF FIGURES

Figure 8B.2.1 LCC Results for Ice-Making Head, Water-Cooled, Small, Batch (IMH-WSmall-B).............................................................................................................8B-2

Figure 8B.2.2 PBP Results for Ice-Making Head, Water-Cooled, Small, Batch (IMH-WSmall-B).............................................................................................................8B-3

Figure 8B.2.3 LCC Results for Ice-Making Head, Water-Cooled, Medium, Batch (IMH-WMed-B)...............................................................................................................8B-4

Figure 8B.2.4 PBP Results for Ice-Making Head, Water-Cooled, Medium, Batch (IMH-WMed-B)...............................................................................................................8B-5

Figure 8B.2.5 LCC Results for Ice-Making Head, Water-Cooled, Large, Batch (smaller, 

1,500 lb unit) (IMH-W-Large-B1).....................................................................8B-6

Figure 8B.2.6 PBP Results for Ice-Making Head, Water-Cooled, Large, Batch (smaller, 

1,500 lb unit) (IMH-W-Large-B1).....................................................................8B-7

8B-ii
Figure 8B.2.7 LCC Results for Ice-Making Head, Water-Cooled, Large, Batch (larger, 2,600 

lb unit) (IMH-W-Large-B2)...............................................................................8B-8

Figure 8B.2.8 PBP Results for Ice-Making Head, Water-Cooled, Large, Batch (larger, 2,600 

lb unit) (IMH-W-Large-B2)...............................................................................8B-9

Figure 8B.2.9 LCC Results for Ice-Making Head, Air-Cooled, Small, Batch (IMH-A-SmallB)......................................................................................................................8B-10

Figure 8B.2.10 PBP Results for Ice-Making Head, Air-Cooled, Small, Batch (IMH-A-SmallB)......................................................................................................................8B-11

Figure 8B.2.11 LCC Results for Ice-Making Head, Air-Cooled, Large, Batch (smaller, 800 lb 

unit) (IMH-A-Large-B1)..................................................................................8B-12

Figure 8B.2.12 PBP Results for Ice-Making Head, Air-Cooled, Large, Batch (smaller, 800 lb 

unit) (IMH-A-Large-B1)..................................................................................8B-13

Figure 8B.2.13 LCC Results for Ice-Making Head, Air-Cooled, Large, Batch (larger, 1,500 

lb unit) (IMH-A-Large-B2)..............................................................................8B-14

Figure 8B.2.14 PBP Results for Ice-Making Head, Air-Cooled, Large, Batch (larger, 1,500 lb 

unit) (IMH-A-Large-B2)..................................................................................8B-15

Figure 8B.2.15 LCC Results for Remote Condensing Unit, Large, Batch (smaller, 1,500 lb 

unit) (RCU-Large-B1) .....................................................................................8B-16

Figure 8B.2.16 PBP Results for Remote Condensing Unit, Large, Batch (smaller, 1,500 lb 

unit) (RCU-Large-B1) .....................................................................................8B-17

Figure 8B.2.17 LCC Results for Remote Condensing Unit, Large, Batch (larger, 2,400 lb 

unit) (RCU-Large-B2) .....................................................................................8B-18

Figure 8B.2.18 PBP Results for Remote Condensing Unit, Large, Batch (larger, 2,400 lb 

unit) (RCU-Large-B2) .....................................................................................8B-19

Figure 8B.2.19 LCC Results for Self-Contained Unit, Water-Cooled, Large, Batch (SCU-WLarge-B)...........................................................................................................8B-20

Figure 8B.2.20 PBP Results for Self-Contained Unit, Water-Cooled, Large, Batch (SCU-WLarge-B)...........................................................................................................8B-21

Figure 8B.2.21 LCC Results for Self-Contained Unit, Air-Cooled, Small, Batch (SCU-ASmall-B)...........................................................................................................8B-22

Figure 8B.2.22 PBP Results for Self-Contained Unit, Air-Cooled, Small, Batch (SCU-ASmall-B)...........................................................................................................8B-23

Figure 8B.2.23 LCC Results for Self-Contained Unit, Air-Cooled, Large, Batch (SCU-ALarge-B)...........................................................................................................8B-24

Figure 8B.2.24 PBP Results for Self-Contained Unit, Air-Cooled, Large, Batch (SCU-ALarge-B)...........................................................................................................8B-25

Figure 8B.2.25 LCC Results for Ice-Making Head, Air-Cooled, Small, Continuous (IMH-ASmall-C)...........................................................................................................8B-26

Figure 8B.2.26 PBP Results for Ice-Making Head, Air-Cooled, Small, Continuous (IMH-ASmall-C)...........................................................................................................8B-27

Figure 8B.2.27 LCC Results for Ice-Making Head, Air-Cooled, Large, Continuous (Smallest, 

820 lb unit) (IMH-A-Large-C).........................................................................8B-28

Figure 8B.2.28 PBP Results for Ice-Making Head, Air-Cooled, Large, Continuous (Smallest, 

820 lb unit) (IMH-A-Large-C).........................................................................8B-29

Figure 8B.2.29 LCC Results for Remote Condensing Unit, Small, Continuous (RCU-SmallC)......................................................................................................................8B-30

8B-iii
Figure 8B.2.30 PBP Results for Remote Condensing Unit, Small, Continuous (RCU-SmallC)......................................................................................................................8B-31

Figure 8B.2.31 LCC Results for Self-Contained Unit, Air-Cooled, Small, Continuous (SCUA-Small-C).......................................................................................................8B-32

Figure 8B.2.32 PBP Results for Self-Contained Unit, Air-Cooled, Small, Continuous (SCUA-Small-C).......................................................................................................8B-33

8B-iv
APPENDIX 8B. DETAILED LIFE-CYCLE COST AND PAYBACK PERIOD ANALYSIS 

RESULTS

8B.1 INTRODUCTION

This appendix contains detailed output from the life-cycle cost (LCC) and payback period 

(PBP) analysis for automatic commercial ice makers, as developed using the LCC spreadsheet.

LCC and PBPs are presented for each of the primary equipment classes for each efficiency level.

See chapter 8 of the technical support document (TSD) for an explanation of the LCC 

spreadsheet.

LCC savings output obtained from the LCC analysis are in the form of distributions. LCC 

savings distributions were generated by performing 10,000 separate simulations using the LCC 

spreadsheet, and allowing the spreadsheet to select values for numerous inputs given probability 

distributions for each input. Given the results of the LCC model simulations, for each equipment 

type, the U.S. Department of Energy (DOE) calculated summary statistics—average values for 

LCC, product installation costs and operating costs; a distribution of customers who: (1) receive 

benefits in the form of lower LCC results; (2) are not affected by the standards; or (3) experience 

a cost in the form of increased LCC from equipment ownership. Results of the LCC and 

accompanying PBP analyses are shown in the tables in this appendix.

Also included in this appendix are two graphical presentations for each of the LCC and 

PBP results. One plots LCC savings distribution for all equipment classes analyzed. The figures 

illustrate the mean and median values on the plot with the help of red and blue markers, 

respectively. Additionally, the mean and median values are surrounded by rectangular boxes 

used to represent the 25th and 75th percentile values. The lower edge of the elongated rectangle 

represents 25th percentile, which means that 25 percent of the customers will experience LCC 

savings below the value represented by the bottom of the rectangle if the standard were set at 

level in question. The upper edge of the elongated rectangle represents the 75th percentile. 

Finally, each of the rectangles includes two “tails,” or two vertical black lines, extending 

downward to represent the 5th percentile (lower end) and upward to represent the 95th percentile 

(upper end).

To simplify the presentation, all efficiency levels are presented as numbered levels. 

Therefore, the efficiency level referred to as the baseline in other chapters of this TSD is 

presented as level 1. All higher efficiency levels are numbered sequentially, from level 1 to level 

7.

The remainder of this appendix presents the tabular and graphical results for the 

equipment classes analyzed for the automatic commercial ice maker rulemaking.

8B-1
8B.2 LIFE CYCLE COST RESULTS

8B.2.1 LIFE-CYCLE COST AND PAYBACK PERIOD RESULTS FOR ICE-MAKING 

HEAD, WATER-COOLED, SMALL, BATCH (IMH-W-SMALL-B)

Table 8B.2.1 LCC and PBP Results for Ice-Making Head, Water-Cooled, Small, Batch 

(IMH-W-Small-B)

Figure 8B.2.1 LCC Results for Ice-Making Head, Water-Cooled, Small, Batch (IMH-WSmall-B)

Net Cost No Impact Net Benefit

kWh/year 2013$ 2013$ 2013$ 2013$ % % % Years

1 2,832 2,401 9,782 12,183 0 NA NA NA NA

2 2,551 2,476 9,533 12,009 175 0 63 37 2.5

3 2,411 2,537 9,381 11,918 214 1 47 52 2.7

4 2,270 2,903 9,279 12,182 -148 82 3 16 8.1

4a 2,227 3,123 9,248 12,370 -333 92 0 8 10.9

5 2,162 3,371 9,200 12,571 -534 96 0 4 13.4

6 NA NA NA NA NA NA NA NA NA

 Payback 

Period, 

Median Efficiency 

Level 

Number

Efficiency 

Level 

Life-Cycle Cost, All Customers Life-Cycle Cost Savings

% of Customers that Experience

Installed

Cost

Total 

Discounted 

Operating 

Cost

LCC, All 

Customers

Affected 

Customers' 

Average 

Savings

-$1,000

-$800

-$600

-$400

-$200

$0

$200

$400

$600

$800

2 3 4 4a 5 6

Cost Savings (2013$)

Efficiency Level

Life-Cycle Cost Savings

Mean Median

8B-2
Figure 8B.2.2 PBP Results for Ice-Making Head, Water-Cooled, Small, Batch (IMH-WSmall-B)

0.00

5.00

10.00

15.00

20.00

25.00

2 3 4 4a 5 6

Years

Efficiency Level

Payback Period 

Median Mean

8B-3
8B.2.2 LIFE-CYCLE COST AND PAYBACK PERIOD RESULTS FOR ICE-MAKING 

HEAD, WATER-COOLED, MEDIUM, BATCH (IMH-W-MED-B)

Table 8B.2.2 LCC and PBP Results for Ice-Making Head, Water-Cooled, Medium, Batch 

(IMH-W-Med-B)

Figure 8B.2.3 LCC Results for Ice-Making Head, Water-Cooled, Medium, Batch (IMH-WMed-B)

Net Cost No Impact Net Benefit

kWh/year 2013$ 2013$ 2013$ 2013$ % % % Years

1 6,040 4,216 21,888 26,104 0 NA NA NA NA

2 5,439 4,325 21,470 25,795 308 0 44 56 2.1

3 5,138 4,607 21,251 25,857 165 28 24 47 5.0

4 4,951 4,943 21,115 26,058 -63 65 9 26 7.6

5 NA NA NA NA NA NA NA NA NA

6 NA NA NA NA NA NA NA NA NA

7 NA NA NA NA NA NA NA NA NA

 Payback 

Period, 

Median Efficiency 

Level 

Number

Efficiency 

Level 

Life-Cycle Cost, All Customers Life-Cycle Cost Savings

% of Customers that Experience

Installed

Cost

Total 

Discounted 

Operating 

Cost

LCC, All 

Customers

Affected 

Customers' 

Average 

Savings

-$600

-$400

-$200

$0

$200

$400

$600

$800

$1,000

2 3 4 5 6 7

Cost Savings (2013$)

Efficiency Level

Life-Cycle Cost Savings

Mean Median

8B-4
Figure 8B.2.4 PBP Results for Ice-Making Head, Water-Cooled, Medium, Batch (IMH-WMed-B)

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

16.00

2 3 4 5 6 7

Years

Efficiency Level

Payback Period 

Median Mean

8B-5
8B.2.3 LIFE-CYCLE COST AND PAYBACK PERIOD RESULTS FOR ICE-MAKING 

HEAD, WATER-COOLED, LARGE, BATCH (SMALLER, 1,500 LB UNIT) 

(IMH-W-LARGE-B1)

Table 8B.2.3 LCC and PBP Results for Ice-Making Head, Water-Cooled, Large, Batch 

(smaller, 1,500 lb unit) (IMH-W-Large-B1)

Figure 8B.2.5 LCC Results for Ice-Making Head, Water-Cooled, Large, Batch (smaller, 

1,500 lb unit) (IMH-W-Large-B1)

Net Cost No Impact Net Benefit

kWh/year 2013$ 2013$ 2013$ 2013$ % % % Years

1 9,166 5,004 37,051 42,055 0 NA NA NA NA

2 8,405 5,747 36,509 42,256 -200 70 13 17 11.1

3 NA NA NA NA NA NA NA NA NA

4 NA NA NA NA NA NA NA NA NA

5 NA NA NA NA NA NA NA NA NA

6 NA NA NA NA NA NA NA NA NA

7 NA NA NA NA NA NA NA NA NA

 Payback 

Period, 

Median Efficiency 

Level 

Number

Efficiency 

Level 

Life-Cycle Cost, All Customers Life-Cycle Cost Savings

% of Customers that Experience

Installed

Cost

Total 

Discounted 

Operating 

Cost

LCC, All 

Customers

Affected 

Customers' 

Average 

Savings

-$600

-$500

-$400

-$300

-$200

-$100

$0

$100

$200

$300

$400

2 3 4 5 6 7

Cost Savings (2013$)

Efficiency Level

Life-Cycle Cost Savings

Mean Median

8B-6
Figure 8B.2.6 PBP Results for Ice-Making Head, Water-Cooled, Large, Batch (smaller, 

1,500 lb unit) (IMH-W-Large-B1)

0.00

5.00

10.00

15.00

20.00

25.00

2 3 4 5 6 7

Years

Efficiency Level

Payback Period 

Median Mean

8B-7
8B.2.4 LIFE-CYCLE COST AND PAYBACK PERIOD RESULTS FOR ICE-MAKING 

HEAD, WATER-COOLED, LARGE, BATCH (LARGER, 2,600 LB UNIT) 

(IMH-W-LARGE-B2)

Table 8B.2.4 LCC and PBP Results for Ice-Making Head, Water-Cooled, Large, Batch 

(larger, 2,600 lb unit) (IMH-W-Large-B2)

Figure 8B.2.7 LCC Results for Ice-Making Head, Water-Cooled, Large, Batch (larger, 

2,600 lb unit) (IMH-W-Large-B2)

Net Cost No Impact Net Benefit

kWh/year 2013$ 2013$ 2013$ 2013$ % % % Years

1 15,868 9,763 62,182 71,945 0 NA NA NA NA

2 14,693 10,681 61,346 72,027 -80 59 13 29 8.9

3 NA NA NA NA NA NA NA NA NA

4 NA NA NA NA NA NA NA NA NA

5 NA NA NA NA NA NA NA NA NA

6 NA NA NA NA NA NA NA NA NA

7 NA NA NA NA NA NA NA NA NA

 Payback 

Period, 

Median Efficiency 

Level 

Number

Efficiency 

Level 

Life-Cycle Cost, All Customers Life-Cycle Cost Savings

% of Customers that Experience

Installed

Cost

Total 

Discounted 

Operating 

Cost

LCC, All 

Customers

Affected 

Customers' 

Average 

Savings

-$800

-$600

-$400

-$200

$0

$200

$400

$600

$800

$1,000

2 3 4 5 6 7

Cost Savings (2013$)

Efficiency Level

Life-Cycle Cost Savings

Mean Median

8B-8
Figure 8B.2.8 PBP Results for Ice-Making Head, Water-Cooled, Large, Batch (larger, 

2,600 lb unit) (IMH-W-Large-B2)

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

16.00

18.00

2 3 4 5 6 7

Years

Efficiency Level

Payback Period 

Median Mean

8B-9
8B.2.5 LIFE-CYCLE COST AND PAYBACK PERIOD RESULTS FOR ICE-MAKING 

HEAD, AIR-COOLED, SMALL, BATCH (IMH-A-SMALL-B)

Table 8B.2.5 LCC and PBP Results for Ice-Making Head, Air-Cooled, Small, Batch (IMHA-Small-B)

Figure 8B.2.9 LCC Results for Ice-Making Head, Air-Cooled, Small, Batch (IMH-A-SmallB)

Net Cost No Impact Net Benefit

kWh/year 2013$ 2013$ 2013$ 2013$ % % % Years

1 3,535 2,442 8,654 11,096 0 NA NA NA NA

2 3,184 2,539 8,420 10,959 136 1 76 22 3.4

3 3,009 2,655 8,293 10,948 72 21 47 32 4.8

3a 2,901 2,695 8,214 10,909 77 21 0 79 4.7

4 2,833 2,865 8,189 11,054 -68 82 0 18 7.3

5 2,658 3,324 8,062 11,386 -400 95 0 5 12.1

6 2,640 3,331 8,048 11,379 -393 95 0 5 11.9

 Payback 

Period, 

Median Efficiency 

Level 

Number

Efficiency 

Level 

Life-Cycle Cost, All Customers Life-Cycle Cost Savings

% of Customers that Experience

Installed

Cost

Total 

Discounted 

Operating 

Cost

LCC, All 

Customers

Affected 

Customers' 

Average 

Savings

-$800

-$600

-$400

-$200

$0

$200

$400

$600

2 3 3a 4 5 6

Cost Savings (2013$)

Efficiency Level

Life-Cycle Cost Savings

Mean Median

8B-10
Figure 8B.2.10 PBP Results for Ice-Making Head, Air-Cooled, Small, Batch (IMH-ASmall-B)

0.00

5.00

10.00

15.00

20.00

25.00

2 3 3a 4 5 6

Years

Efficiency Level

Payback Period 

Median Mean

8B-11
8B.2.6 LIFE-CYCLE COST AND PAYBACK PERIOD RESULTS FOR ICE-MAKING 

HEAD, AIR-COOLED, LARGE, BATCH (SMALLER, 800 LB UNIT) (IMH-ALARGE-B1)

Table 8B.2.6 LCC and PBP Results for Ice-Making Head, Air-Cooled, Large, Batch 

(smaller, 800 lb unit) (IMH-A-Large-B1)

Figure 8B.2.11 LCC Results for Ice-Making Head, Air-Cooled, Large, Batch (smaller, 800 

lb unit) (IMH-A-Large-B1)

Net Cost No Impact Net Benefit

kWh/year 2013$ 2013$ 2013$ 2013$ % % % Years

1 7,350 4,097 14,454 18,550 0 NA NA NA NA

2 6,617 4,172 13,943 18,115 439 0 66 34 1.2

3 6,251 4,269 13,506 17,775 580 0 38 62 1.5

3a 6,192 4,275 13,464 17,738 407 0 3 97 1.5

4 5,885 4,602 13,247 17,850 294 35 3 63 3.4

5 5,636 5,025 13,066 18,091 45 61 0 39 5.4

6 NA NA NA NA NA NA NA NA NA

 Payback 

Period, 

Median Efficiency 

Level 

Number

Efficiency 

Level 

Life-Cycle Cost, All Customers Life-Cycle Cost Savings

% of Customers that Experience

Installed

Cost

Total 

Discounted 

Operating 

Cost

LCC, All 

Customers

Affected 

Customers' 

Average 

Savings

-$1,000

-$500

$0

$500

$1,000

$1,500

$2,000

2 3 3a 4 5 6

Cost Savings (2013$)

Efficiency Level

Life-Cycle Cost Savings

Mean Median

8B-12
Figure 8B.2.12 PBP Results for Ice-Making Head, Air-Cooled, Large, Batch (smaller, 800 

lb unit) (IMH-A-Large-B1)

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

9.00

10.00

2 3 3a 4 5 6

Years

Efficiency Level

Payback Period 

Median Mean

8B-13
8B.2.7 LIFE-CYCLE COST AND PAYBACK PERIOD RESULTS FOR ICE-MAKING 

HEAD, AIR-COOLED, LARGE, BATCH (LARGER, 1,500 LB UNIT) (IMH-ALARGE-B2)

Table 8B.2.7 LCC and PBP Results for Ice-Making Head, Air-Cooled, Large, Batch 

(larger, 1,500 lb unit) (IMH-A-Large-B2)

Figure 8B.2.13 LCC Results for Ice-Making Head, Air-Cooled, Large, Batch (larger, 1,500 

lb unit) (IMH-A-Large-B2)

Net Cost No Impact Net Benefit

kWh/year 2013$ 2013$ 2013$ 2013$ % % % Years

1 11,999 4,465 18,958 23,424 0 NA NA NA NA

2 10,802 5,222 18,129 23,350 76 9 83 8 7.4

3 10,591 5,298 17,975 23,273 110 10 61 29 6.9

4 NA NA NA NA NA NA NA NA NA

5 NA NA NA NA NA NA NA NA NA

6 NA NA NA NA NA NA NA NA NA

7 NA NA NA NA NA NA NA NA NA

 Payback 

Period, 

Median Efficiency 

Level 

Number

Efficiency 

Level 

Life-Cycle Cost, All Customers Life-Cycle Cost Savings

% of Customers that Experience

Installed

Cost

Total 

Discounted 

Operating 

Cost

LCC, All 

Customers

Affected 

Customers' 

Average 

Savings

-$600

-$400

-$200

$0

$200

$400

$600

$800

$1,000

$1,200

2 3 4 5 6 7

Cost Savings (2013$)

Efficiency Level

Life-Cycle Cost Savings

Mean Median

8B-14
Figure 8B.2.14 PBP Results for Ice-Making Head, Air-Cooled, Large, Batch (larger, 1,500 

lb unit) (IMH-A-Large-B2)

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

16.00

2 3 4 5 6 7

Years

Efficiency Level

Payback Period 

Median Mean

8B-15
8B.2.8 LIFE-CYCLE COST AND PAYBACK PERIOD RESULTS FOR REMOTE 

CONDENSING UNIT, LARGE, BATCH (SMALLER, 1,500 LB UNIT) (RCULARGE-B1) 

Table 8B.2.8 LCC and PBP Results for Remote Condensing Unit, Large, Batch (smaller, 

1,500 lb unit) (RCU-Large-B1)

Figure 8B.2.15 LCC Results for Remote Condensing Unit, Large, Batch (smaller, 1,500 lb 

unit) (RCU-Large-B1)

Net Cost No Impact Net Benefit

kWh/year 2013$ 2013$ 2013$ 2013$ % % % Years

1 11,679 6,122 15,026 21,148 0 NA NA NA NA

2 10,514 6,220 14,190 20,410 743 0 56 44 0.9

3 9,931 6,635 13,790 20,425 391 25 20 55 3.4

4 9,664 6,985 13,595 20,580 161 55 1 44 4.9

5 NA NA NA NA NA NA NA NA NA

6 NA NA NA NA NA NA NA NA NA

7 NA NA NA NA NA NA NA NA NA

 Payback 

Period, 

Median Efficiency 

Level 

Number

Efficiency 

Level 

Life-Cycle Cost, All Customers Life-Cycle Cost Savings

% of Customers that Experience

Installed

Cost

Total 

Discounted 

Operating 

Cost

LCC, All 

Customers

Affected 

Customers' 

Average 

Savings

-$1,000

-$500

$0

$500

$1,000

$1,500

$2,000

2 3 4 5 6 7

Cost Savings (2013$)

Efficiency Level

Life-Cycle Cost Savings

Mean Median

8B-16
Figure 8B.2.16 PBP Results for Remote Condensing Unit, Large, Batch (smaller, 1,500 lb 

unit) (RCU-Large-B1)

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

9.00

10.00

2 3 4 5 6 7

Years

Efficiency Level

Payback Period 

Median Mean

8B-17
8B.2.9 LIFE-CYCLE COST AND PAYBACK PERIOD RESULTS FOR REMOTE 

CONDENSING UNIT, LARGE, BATCH (LARGER, 2,400 LB UNIT) (RCULARGE-B2)

Table 8B.2.9 LCC and PBP Results for Remote Condensing Unit, Large, Batch (larger, 

2,400 lb unit) (RCU-Large-B2)

Figure 8B.2.17 LCC Results for Remote Condensing Unit, Large, Batch (larger, 2,400 lb 

unit) (RCU-Large-B2)

Net Cost No Impact Net Benefit

kWh/year 2013$ 2013$ 2013$ 2013$ % % % Years

1 18,671 8,999 21,819 30,818 0 NA NA NA NA

2 16,807 9,465 20,540 30,005 820 1 56 43 3.0

3 16,077 10,516 20,046 30,562 -109 57 20 23 7.0

4 NA NA NA NA NA NA NA NA NA

5 NA NA NA NA NA NA NA NA NA

6 NA NA NA NA NA NA NA NA NA

7 NA NA NA NA NA NA NA NA NA

 Payback 

Period, 

Median Efficiency 

Level 

Number

Efficiency 

Level 

Life-Cycle Cost, All Customers Life-Cycle Cost Savings

% of Customers that Experience

Installed

Cost

Total 

Discounted 

Operating 

Cost

LCC, All 

Customers

Affected 

Customers' 

Average 

Savings

-$1,000

-$500

$0

$500

$1,000

$1,500

$2,000

$2,500

2 3 4 5 6 7

Cost Savings (2013$)

Efficiency Level

Life-Cycle Cost Savings

Mean Median

8B-18
Figure 8B.2.18 PBP Results for Remote Condensing Unit, Large, Batch (larger, 2,400 lb 

unit) (RCU-Large-B2)

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

2 3 4 5 6 7

Years

Efficiency Level

Payback Period 

Median Mean

8B-19
8B.2.10 LIFE-CYCLE COST AND PAYBACK PERIOD RESULTS FOR SELFCONTAINED UNIT, WATER-COOLED, LARGE, BATCH (SCU-W-LARGE-B)

Table 8B.2.10 LCC and PBP Results for Self-Contained Unit, Water-Cooled, Large, Batch 

(SCU-W-Large-B)

Figure 8B.2.19 LCC Results for Self-Contained Unit, Water-Cooled, Large, Batch (SCUW-Large-B)

Net Cost No Impact Net Benefit

kWh/year 2013$ 2013$ 2013$ 2013$ % % % Years

1 3,499 3,467 11,136 14,603 0 NA NA NA NA

2 3,151 3,540 10,617 14,158 444 0 28 72 1.1

3 2,978 3,580 10,491 14,071 527 0 28 72 1.4

4 2,804 3,620 10,364 13,984 613 0 28 72 1.6

5 2,630 3,664 10,238 13,902 550 0 5 94 1.8

6 2,464 4,114 10,117 14,231 192 44 0 56 5.1

7 NA NA NA NA NA NA NA NA NA

 Payback 

Period, 

Median Efficiency 

Level 

Number

Efficiency 

Level 

Life-Cycle Cost, All Customers Life-Cycle Cost Savings

% of Customers that Experience

Installed

Cost

Total 

Discounted 

Operating 

Cost

LCC, All 

Customers

Affected 

Customers' 

Average 

Savings

-$500

$0

$500

$1,000

$1,500

$2,000

2 3 4 5 6 7

Cost Savings (2013$)

Efficiency Level

Life-Cycle Cost Savings

Mean Median

8B-20
Figure 8B.2.20 PBP Results for Self-Contained Unit, Water-Cooled, Large, Batch (SCU-WLarge-B)

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

9.00

10.00

2 3 4 5 6 7

Years

Efficiency Level

Payback Period 

Median Mean

8B-21
8B.2.11 LIFE-CYCLE COST AND PAYBACK PERIOD RESULTS FOR SELFCONTAINED UNIT, AIR-COOLED, SMALL, BATCH (SCU-A-SMALL-B)

Table 8B.2.11 LCC and PBP Results for Self-Contained Unit, Air-Cooled, Small, Batch 

(SCU-A-Small-B)

Figure 8B.2.21 LCC Results for Self-Contained Unit, Air-Cooled, Small, Batch (SCU-ASmall-B)

Net Cost No Impact Net Benefit

kWh/year 2013$ 2013$ 2013$ 2013$ % % % Years

1 2,177 2,759 7,344 10,103 0 NA NA NA NA

2 1,962 2,799 7,193 9,992 110 0 48 52 2.2

3 1,854 2,822 7,122 9,944 134 1 33 66 2.3

4 1,747 2,845 7,051 9,896 161 1 20 79 2.4

5 1,639 2,918 6,843 9,761 281 1 12 87 2.6

6 1,532 3,000 6,778 9,778 230 16 0 84 3.5

7 1,473 3,416 6,737 10,153 -145 77 0 23 8.9

 Payback 

Period, 

Median Efficiency 

Level 

Number

Efficiency 

Level 

Life-Cycle Cost, All Customers Life-Cycle Cost Savings

% of Customers that Experience

Installed

Cost

Total 

Discounted 

Operating 

Cost

LCC, All 

Customers

Affected 

Customers' 

Average 

Savings

-$600

-$400

-$200

$0

$200

$400

$600

$800

$1,000

2 3 4 5 6 7

Cost Savings (2013$)

Efficiency Level

Life-Cycle Cost Savings

Mean Median

8B-22
Figure 8B.2.22 PBP Results for Self-Contained Unit, Air-Cooled, Small, Batch (SCU-ASmall-B)

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

16.00

18.00

2 3 4 5 6 7

Years

Efficiency Level

Payback Period 

Median Mean

8B-23
8B.2.12 LIFE-CYCLE COST AND PAYBACK PERIOD RESULTS FOR SELFCONTAINED UNIT, AIR-COOLED, LARGE, BATCH (SCU-A-LARGE-B)

Table 8B.2.12 LCC and PBP Results for Self-Contained Unit, Air-Cooled, Large, Batch 

(SCU-A-Large-B)

Figure 8B.2.23 LCC Results for Self-Contained Unit, Air-Cooled, Large, Batch (SCU-ALarge-B)

Net Cost No Impact Net Benefit

kWh/year 2013$ 2013$ 2013$ 2013$ % % % Years

1 3,011 3,227 10,280 13,507 0 NA NA NA NA

2 2,713 3,275 10,070 13,344 163 0 37 63 1.8

3 2,563 3,301 9,969 13,269 206 0 23 77 1.9

4 2,414 3,345 9,685 13,030 400 0 1 99 1.6

5 2,265 3,402 9,590 12,992 439 0 1 99 2.1

6 2,141 3,854 9,500 13,355 71 54 0 46 6.5

7 NA NA NA NA NA NA NA NA NA

 Payback 

Period, 

Median Efficiency 

Level 

Number

Efficiency 

Level 

Life-Cycle Cost, All Customers Life-Cycle Cost Savings

% of Customers that Experience

Installed

Cost

Total 

Discounted 

Operating 

Cost

LCC, All 

Customers

Affected 

Customers' 

Average 

Savings

-$400

-$200

$0

$200

$400

$600

$800

$1,000

$1,200

2 3 4 5 6 7

Cost Savings (2013$)

Efficiency Level

Life-Cycle Cost Savings

Mean Median

8B-24
Figure 8B.2.24 PBP Results for Self-Contained Unit, Air-Cooled, Large, Batch (SCU-ALarge-B)

0.00

2.00

4.00

6.00

8.00

10.00

12.00

2 3 4 5 6 7

Years

Efficiency Level

Payback Period 

Median Mean

8B-25
8B.2.13 LIFE-CYCLE COST AND PAYBACK PERIOD RESULTS FOR ICE-MAKING 

HEAD, AIR-COOLED, SMALL, CONTINUOUS (IMH-A-SMALL-C)

Table 8B.2.13 LCC and PBP Results for Ice-Making Head, Air-Cooled, Small, Continuous 

(IMH-A-Small-C)

Figure 8B.2.25 LCC Results for Ice-Making Head, Air-Cooled, Small, Continuous (IMH-ASmall-C)

Net Cost No Impact Net Benefit

kWh/year 2013$ 2013$ 2013$ 2013$ % % % Years

1 4,299 6,620 9,166 15,787 0 NA NA NA NA

2 3,872 6,674 8,869 15,543 245 0 69 31 1.5

3 3,658 6,709 8,723 15,432 292 0 58 42 1.6

4 3,445 6,745 8,572 15,317 313 0 39 61 1.7

5 3,231 7,258 8,553 15,811 -207 54 33 13 8.7

6 3,201 7,264 8,552 15,816 -165 68 14 18 8.8

7 NA NA NA NA NA NA NA NA NA

 Payback 

Period, 

Median Efficiency 

Level 

Number

Efficiency 

Level 

Life-Cycle Cost, All Customers Life-Cycle Cost Savings

% of Customers that Experience

Installed

Cost

Total 

Discounted 

Operating 

Cost

LCC, All 

Customers

Affected 

Customers' 

Average 

Savings

-$800

-$600

-$400

-$200

$0

$200

$400

$600

$800

$1,000

2 3 4 5 6 7

Cost Savings (2013$)

Efficiency Level

Life-Cycle Cost Savings

Mean Median

8B-26
Figure 8B.2.26 PBP Results for Ice-Making Head, Air-Cooled, Small, Continuous (IMH-ASmall-C)

0.00

5.00

10.00

15.00

20.00

25.00

2 3 4 5 6 7

Years

Efficiency Level

Payback Period 

Median Mean

8B-27
8B.2.14 LIFE-CYCLE COST AND PAYBACK PERIOD RESULTS FOR ICE-MAKING 

HEAD, AIR-COOLED, LARGE, CONTINUOUS (SMALLEST, 820 LB UNIT) 

(IMH-A-LARGE-C)

Table 8B.2.14 LCC and PBP Results for Ice-Making Head, Air-Cooled, Large, Continuous 

(Smallest, 820 lb unit) (IMH-A-Large-C)

Figure 8B.2.27 LCC Results for Ice-Making Head, Air-Cooled, Large, Continuous 

(Smallest, 820 lb unit) (IMH-A-Large-C)

Net Cost No Impact Net Benefit

kWh/year 2013$ 2013$ 2013$ 2013$ % % % Years

1 8,270 5,489 14,861 20,350 0 NA NA NA NA

2 7,445 5,538 14,275 19,813 539 0 57 43 0.7

3 7,033 5,568 13,979 19,547 626 0 35 65 0.7

4 6,621 6,287 13,765 20,052 -10 52 17 31 5.9

5 6,348 6,310 13,705 20,015 28 54 9 37 5.9

6 NA NA NA NA NA NA NA NA NA

7 NA NA NA NA NA NA NA NA NA

 Payback 

Period, 

Median Efficiency 

Level 

Number

Efficiency 

Level 

Life-Cycle Cost, All Customers Life-Cycle Cost Savings

% of Customers that Experience

Installed

Cost

Total 

Discounted 

Operating 

Cost

LCC, All 

Customers

Affected 

Customers' 

Average 

Savings

-$1,000

-$500

$0

$500

$1,000

$1,500

$2,000

2 3 4 5 6 7

Cost Savings (2013$)

Efficiency Level

Life-Cycle Cost Savings

Mean Median

8B-28
Figure 8B.2.28 PBP Results for Ice-Making Head, Air-Cooled, Large, Continuous 

(Smallest, 820 lb unit) (IMH-A-Large-C)

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

2 3 4 5 6 7

Years

Efficiency Level

Payback Period 

Median Mean

8B-29
8B.2.15 LIFE-CYCLE COST AND PAYBACK PERIOD RESULTS FOR REMOTE 

CONDENSING UNIT, SMALL, CONTINUOUS (RCU-SMALL-C)

Table 8B.2.15 LCC and PBP Results for Remote Condensing Unit, Small, Continuous 

(RCU-Small-C)

Figure 8B.2.29 LCC Results for Remote Condensing Unit, Small, Continuous (RCU-SmallC)

Net Cost No Impact Net Benefit

kWh/year 2013$ 2013$ 2013$ 2013$ % % % Years

1 7,737 5,641 9,134 14,776 0 NA NA NA NA

2 6,966 5,690 8,588 14,278 498 0 72 28 0.7

3 6,580 5,758 8,319 14,078 448 0 44 55 1.2

4 6,195 5,808 8,046 13,854 505 0 11 89 1.2

5 5,809 6,495 7,875 14,370 -41 63 6 32 5.5

6 5,688 6,523 7,878 14,402 -73 64 6 31 5.8

7 NA NA NA NA NA NA NA NA NA

 Payback 

Period, 

Median Efficiency 

Level 

Number

Efficiency 

Level 

Life-Cycle Cost, All Customers Life-Cycle Cost Savings

% of Customers that Experience

Installed

Cost

Total 

Discounted 

Operating 

Cost

LCC, All 

Customers

Affected 

Customers' 

Average 

Savings

-$1,000

-$500

$0

$500

$1,000

$1,500

$2,000

2 3 4 5 6 7

Cost Savings (2013$)

Efficiency Level

Life-Cycle Cost Savings

Mean Median

8B-30
Figure 8B.2.30 PBP Results for Remote Condensing Unit, Small, Continuous (RCU-SmallC)

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

2 3 4 5 6 7

Years

Efficiency Level

Payback Period 

Median Mean

8B-31
8B.2.16 LIFE-CYCLE COST AND PAYBACK PERIOD RESULTS FOR SELFCONTAINED UNIT, AIR-COOLED, SMALL, CONTINUOUS (SCU-A-SMALLC)

Table 8B.2.16 LCC and PBP Results for Self-Contained Unit, Air-Cooled, Small, 

Continuous (SCU-A-Small-C)

Figure 8B.2.31 LCC Results for Self-Contained Unit, Air-Cooled, Small, Continuous (SCUA-Small-C)

Net Cost No Impact Net Benefit

kWh/year 2013$ 2013$ 2013$ 2013$ % % % Years

1 3,416 3,598 8,421 12,020 0 NA NA NA NA

2 3,077 3,622 8,175 11,797 224 0 56 44 0.8

3 2,907 3,646 8,059 11,705 278 0 47 53 1.1

4 2,738 3,685 7,948 11,633 290 1 32 67 1.5

5 2,568 4,216 7,938 12,154 -291 73 15 12 10.7

6 2,515 4,224 7,950 12,174 -268 86 0 14 11.4

7 NA NA NA NA NA NA NA NA NA

 Payback 

Period, 

Median Efficiency 

Level 

Number

Efficiency 

Level 

Life-Cycle Cost, All Customers Life-Cycle Cost Savings

% of Customers that Experience

Installed

Cost

Total 

Discounted 

Operating 

Cost

LCC, All 

Customers

Affected 

Customers' 

Average 

Savings

-$800

-$600

-$400

-$200

$0

$200

$400

$600

$800

$1,000

2 3 4 5 6 7

Cost Savings (2013$)

Efficiency Level

Life-Cycle Cost Savings

Mean Median

8B-32
Figure 8B.2.32 PBP Results for Self-Contained Unit, Air-Cooled, Small, Continuous (SCUA-Small-C)

0.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

2 3 4 5 6 7

Years

Efficiency Level

Payback Period 

Median Mean

8B-33
APPENDIX 8C. UNCERTAINTY AND VARIABILITY IN LIFE-CYCLE COST 

ANALYSIS

TABLE OF CONTENTS

8C.1 INTRODUCTION .........................................................................................................8C-1

8C.2 UNCERTAINTY ...........................................................................................................8C-1

8C.3 VARIABILITY..............................................................................................................8C-1

8C.4 APPROACHES TO UNCERTAINTY AND VARIABILITY .....................................8C-1

8C.5 PROBABILITY ANALYSIS AND THE USE OF CRYSTAL BALL.........................8C-2

8C-i
APPENDIX 8C. UNCERTAINTY AND VARIABILITY IN LIFE-CYCLE COST

ANALYSIS

8C.1 INTRODUCTION

The U.S. Department of Energy’s (DOE’s) approach to analysis of an energy-efficiency 

standard also includes analyzing the range of potential impacts of higher efficiency equipment on 

customers. DOE uses the customer life-cycle cost (LCC) as the key metric for this analysis and 

examines the variability of the LCC of higher efficiency equipment by varying a wide range of 

input assumptions. To perform the calculation, analysts must first: (1) specify the equation or 

model that will be used; (2) define the quantities in the equation; and (3) provide numerical 

values for each quantity. In the simplest case, the equation is unambiguous (contains all relevant 

quantities and no others), each quantity has a single numerical value, and the calculation results 

in a single value. However, in most cases, the model and/or the numerical values for each

quantity in the model are not completely known (i.e., there is uncertainty), and/or the numerical 

values for each quantity in the model depend upon other conditions (i.e., there is variability).

Thorough analysis involves accounting for uncertainty and variability. While the simplest 

analysis involves a single numerical value for each quantity in a calculation, arguments can arise 

about what the appropriate value is for each quantity. Explicit analysis of uncertainty and 

variability is intended to provide more complete information to the decision-making process for 

establishing standards for automatic commercial ice-making (ACIM) equipment.

8C.2 UNCERTAINTY

When making observations of past events or speculating about the future, imperfect

knowledge is the rule rather than the exception. For example, the lifetime of an ice maker used in 

a restaurant is not available at the time of purchase, but rather estimated based upon available 

historical information. When estimating numerical values expected for quantities at some future 

date, the exact outcome is rarely known in advance.

8C.3 VARIABILITY

Variability means that different applications or situations produce different numerical

values when calculating a quantity. Specifying an exact value for a quantity may be difficult

because the value depends on another parameter. For example, the electricity price faced by a 

customer is dependent on the state where the customer’s business is located and the type of 

business. 

8C.4 APPROACHES TO UNCERTAINTY AND VARIABILITY

This section describes two approaches to uncertainty and variability: scenario analysis 

and probability analysis.

Scenario analysis uses a single numerical value for each quantity in a calculation, then

changes one (or more) of the numerical values and repeats the calculation. A number of

calculations are done, which provide some indication of the extent to which the result depends on 

8C-1
the assumptions. The advantages of scenario analysis are that each calculation is simple, a range 

of estimates is used, and crossover points can be identified. (An example of a crossover point is 

the energy price rate at which the customer achieves savings in operating expense that more than 

compensate for the increased purchase expense.) The disadvantage of scenario analysis is that 

there is no information about the likelihood of each scenario.

Probability analysis considers the probabilities within a range of values. For quantities

with variability (e.g., electricity prices paid by different users), surveys can be used to generate a

frequency distribution of numerical values (e.g., the number of commercial buildings with 

electricity prices at particular levels) to estimate the probability of each value. For quantities with 

uncertainty, statistical or subjective measures can be used to provide probabilities (e.g.,

manufacturing cost to improve energy efficiency to some level may be estimated to be 

$100 ± $20). The major disadvantage of the probability approach is that it requires more 

information, namely information about the shapes and magnitudes of the variability and 

uncertainty of each quantity. The advantage of the probability approach is that it provides greater 

information about the outcome of the calculations (i.e., it provides the probability that the 

outcome will be in a particular range). Scenario and probability analyses provide some indication 

of the robustness of the policy given the uncertainties and variability. A policy is robust when the 

impacts are acceptable over a wide range of possible conditions.

8C.5 PROBABILITY ANALYSIS AND THE USE OF CRYSTAL BALL

To quantify the uncertainty and variability that exist in inputs to the engineering, LCC,

and payback period analyses, DOE used Microsoft Excel spreadsheets combined with Crystal 

Ball, a commercially available add-in, to conduct probability analyses. The probability analysis 

is carried out in the form of Monte Carlo simulations. Simulation refers to any analytical method 

meant to imitate a real-life system, especially when other analyses are mathematically complex 

or difficult to reproduce. Without the aid of simulation, a spreadsheet model will only reveal a 

single outcome, generally the most likely or average scenario. 

Monte Carlo simulations are carried out by performing the LCC analysis multiple times 

(10,000 times for each equipment class for this rulemaking). Each simulation has one particular 

set of inputs. The inputs are chosen from their respective probability distributions. For example, 

there are 50 states in the United States and the probability of choosing one state for a simulation 

is based on the population of the state. The higher the population of a state, the higher the 

probability of choosing that state for a particular simulation. Similarly, the equipment lifetime is 

specified by Weibull distribution curves with mean an equipment lifetime of 8.5 years. Crystal 

Ball chooses the equipment lifetime for any particular simulation based on the Weibull 

probability distribution curve. Thus, each Monte Carlo simulation is a random combination of 

input values, with each input value chosen according to its respective probability distribution.

That is, during a single trial, Crystal Ball selects a value from the defined possibilities (the range 

and shape of the probability distribution) for each uncertain variable and then recalculates the 

spreadsheet.

8C-2
APPENDIX 8D. ESTIMATION OF POTENTIAL EQUIPMENT PRICE TRENDS FOR 

AUTOMATIC COMMERCIAL ICE MAKERS

TABLE OF CONTENTS

8D.1 INTRODUCTION ........................................................................................................... 8D-1

8D.2 DATA EVALUATION AND ANALYSIS..................................................................... 8D-2

LIST OF FIGURES

Figure 8D.1.1 PPI Data for Automatic Commercial Ice Makers.............................................. 8D-2

Figure 8D.2.1 Projected Annual Shipments for Automatic Commercial Ice Makers.............. 8D-3

Figure 8D.2.2 Model Fit for Automatic Commercial Ice Makers............................................ 8D-4

8D-i
APPENDIX 8D. ESTIMATION OF POTENTIAL EQUIPMENT PRICE TRENDS FOR 

AUTOMATIC COMMERCIAL ICE MAKERS

8D.1 INTRODUCTION

In developing the proposed standards, the U.S. Department of Energy (DOE) assumes

that the manufacturer costs and retail prices of products meeting various efficiency levels remain 

fixed, in real terms, after 2013 (the year for which the engineering analysis estimated costs) and

throughout the period of the analysis. In its notice of data availability (NODA) published on 

February 22, 2011, DOE stated that it may consider improving regulatory analysis by addressing 

equipment price trends. 76 FR at 9696. Consistent with the NODA, DOE examined historical 

producer price indices for automatic commercial ice makers.

DOE stated in the NODA that examination of historical price data for certain appliances 

and equipment that have been subject to energy conservation standards indicates that the 

assumption of constant real prices and costs may, in many cases, overestimate long-term 

appliance and equipment price trends. 76 FR 9696 (Feb. 22, 2011). Economic literature and 

historical data suggest that the real costs of these products may in fact trend downward over time 

according to “learning” or “experience” curves, or alternatively that the price trends for certain 

sectors of the U.S. economy may be different than the price trends for the economy as a whole.

A draft paper, Using the Experience Curve Approach for Appliance Price Forecasting, available

at www1.eere.energy.gov/buildings/appliance_standards/pdfs/experience_curve_appliance

_price_forecasting_3-16-11.pdf summarizes the data and literature currently available to DOE 

that is relevant to price forecasts for selected appliances and equipment. 

The extensive literature on the “learning” or “experience” curve phenomenon is typically 

based on observations in the manufacturing sector.1 In the experience curve method, the real cost 

of production is related to the cumulative production or “experience” with a manufactured 

product. To explain the empirical relationship, DOE would use the theory of technology learning

to substantiate a decline in the cost of producing a given product as firms accumulate experience 

with the technology. A common functional relationship used to model the evolution of 

production costs in this case is:

Y = aX-b

Eq. 8D.1

Where:

a = an initial price (or cost), 

b = a positive constant known as the learning rate parameter, 

X = cumulative production, and 

Y = the price as a function of cumulative production. 

Thus, as experience (production) accumulates, the cost of producing the next unit 

decreases. The percentage reduction in cost that occurs with each doubling of cumulative 

production is known as the learning rate (LR), given by:

8D-1
LR = 1 – 2-b

Eq. 8D.2

In typical learning curve formulations, the learning rate parameter is derived using two 

historical data series: cumulative production and price (or cost). 

DOE examined historical prices using the Bureau of Labor Statistics’ (BLS) Producer 

Price Index (PPI) and gross domestic product (GDP) deflator, available from the Bureau of 

Economic Analysis (BEA). The PPI data for commercial refrigerators and related equipment are

available for 1978–2013 and are used to represent aggregate automatic commercial ice-maker

prices. Figure 8D.1.1 shows the PPI data series used. 

Figure 8D.1.1 PPI Data for Automatic Commercial Ice Makers

Inflation-adjusted price indices were calculated by dividing the fitted PPI series by the 

GDP deflator for the same years. The GDP deflator was used as opposed to the Consumer Price 

Index because nearly all automatic commercial ice makers are shipped to commercial customers,

and use of the GDP deflator was consistent with energy price forecast assumptions by the Energy 

Information Administration (EIA).

8D.2 DATA EVALUATION AND ANALYSIS

Figure 8D.1.1 shows an apparent price trend in automatic commercial ice makers that is 

trending slightly upward from 1978 to 2013, but shows a decrease in the real PPI during two 

significant periods of time: 1982 to 1988 and1992 to 2004. Based on these price trends, DOE 

expects that the PPI is likely to resume a downward trend in the future.

8D-2
To perform an experience curve fit, DOE assembled a time-series of annual shipments for 

1978 to 2010 for automatic commercial ice makers (for calculating cumulative production) based 

on shipments data from Current Industries Report. 

Projected shipments after 2010 were obtained from the base-case projections made for 

the national impact analysis (see chapter 10 of this technical support document). Projected 

annual shipments are depicted in Figure 8D.2.1.

Figure 8D.2.1 Projected Annual Shipments for Automatic Commercial Ice Makers

To estimate potential product price trends, DOE performed a least-squares power-law fit 

on the commercial refrigeration equipment price index versus cumulative shipments. The form 

of the fitting equation is: 

P(X) = PoX-b

Eq. 8D.3

Where:

b = the learning rate parameter, and 

Po = the price or cost of the first unit of production.

Both b and Po are obtained by fitting the model to the data.

8D-3
DOE notes that the cumulative shipments on the right-hand side of the equation can 

depend on price, so there is an issue with simultaneity where the independent variable is not truly 

independent. DOE’s use of a simple least-squares fit is equivalent to an assumption of no 

significant first price elasticity effects in the cumulative shipments variable. 

After modeling the data to Eq. 8D.3, DOE estimated the learning rate (defined as the 

fractional reduction in price expected from each doubling of cumulative production) as 1.9

percent.

With cumulative shipments through 2047 projected to reach 437,015 units (compared 

with 213,728 units in 2010), the modeled trend predicts a drop of 2.4 percent in real price 

compared to the 2013 prices in the economy as a whole. Figure 8D.2.2 shows the model fit for 

the projected values for the period after 2010. 

Figure 8D.2.2 Model Fit for Automatic Commercial Ice Makers

8D-4
REFERENCES

1. Weiss, M., Junginger, H.M., Patel, M.K., Blok, K. A Review of Experience Curve 

Analyses for Energy Demand Technologies. Technological Forecasting & Social Change. 

2010. 77:411-428.

8D-5
CHAPTER 9. SHIPMENTS ANALYSIS

TABLE OF CONTENTS

9.1 INTRODUCTION .............................................................................................................. 9-1

9.2 SHIPMENTS MODEL....................................................................................................... 9-2

9.2.1 Shipments Model Equations................................................................................... 9-2

9.2.2 Stock Events............................................................................................................ 9-3

9.2.2.1 Replacing Equipment............................................................................... 9-3

9.2.2.2 New Equipment ....................................................................................... 9-4

9.3 DATA INPUTS .................................................................................................................. 9-5

9.3.1 Historical Shipments............................................................................................... 9-5

9.3.2 Historical Shipments and Projected Building Stock Additions.............................. 9-6

9.3.3 Building Space and Market Saturation ................................................................... 9-8

9.3.3.1 Floor Space: New Construction............................................................... 9-8

9.3.3.2 Market Shipments.................................................................................... 9-8

9.4 RESULTS ........................................................................................................................... 9-9

REFERENCES .......................................................................................................................... 9-11

LIST OF TABLES

Table 9.2.1 Probability of Equipment Failure by Year................................................................ 9-4

Table 9.3.1 Distribution of Ice Maker Stock by Building Type .................................................. 9-6

Table 9.3.2 AEO2014 Forecast of Commercial Floor Space Additions by Building Type......... 9-7

Table 9.3.3 ACIM Equipment Shipments, Percent by Equipment Class .................................... 9-7

Table 9.4.1 Forecast Shipments of New and Replacement Automatic Commercial 

Ice Makers by Building Type, 2018−2047 (Base Case)...................................... 9-9

Table 9.4.2 Forecast Shipments of Automatic Commercial Ice Makers, 2018−2047, by 

Equipment Type (Base Case)*........................................................................... 9-10

LIST OF FIGURES

Figure 9.1.1 Flow Chart Showing Inputs to the Shipments Model ............................................. 9-1

9-i
CHAPTER 9. SHIPMENTS ANALYSIS

9.1 INTRODUCTION

Automatic commercial ice-making (ACIM) equipment shipments are key inputs to the 

national energy savings, net present value, and manufacturer impact analyses. This chapter 

describes the U.S. Department of Energy’s (DOE’s) methodology for estimating ACIM

equipment shipments and annual equipment stocks for 2018 through 2047.

DOE’s shipments model predicts ACIM equipment stock flows as a function of year and 

age. Historical shipment data for 13 primary and 12 secondary ACIM equipment classes are key 

inputs to the shipments model. Figure 9.1.1 outlines the structure of the shipments model.

Figure 9.1.1 Flow Chart Showing Inputs to the Shipments Model

The model assumes that, in each year, the existing stock of automatic commercial ice 

makers either ages by 1 year or fails, and that failed equipment is replaced. It also assumes that

new equipment can be installed in new commercial floor space and old equipment can be 

removed through demolitions (not shown in the diagram).

Section 9.2 presents the mathematical formulation of the model; section 9.3 describes the 

data input to the model, and section 9.4 presents the results for the base-case energy conservation 

standard-level scenario and discusses the development of higher standard scenarios.

Initial Shipments to 

New Construction

Initial Stock

Projections

Initial Replacement 

Shipments

Shipments to 

Construction

NEMS Commercial 

Construction Forecast

Initial Stock

Total Shipments by 

Equipment Class

Replacement 

Shipments

Estimated Lifetime of 

ACIM Equipment

Stock by Equipment 

Class

9-1
9.2 SHIPMENTS MODEL

DOE examined all 25 equipment classes using the shipments model. DOE assumed no 

interdependence between the shipments of the various equipment classes; thus, the equations are 

applicable to each equipment class independently.

As described in sections 9.3.1 and 9.3.2, DOE collected historical ACIM equipment 

shipment data from 1994 through 2010. Using the historical data aggregated over 8.5-year 

periods, DOE developed a multi-year series of estimates of ACIM equipment stocks. DOE used

the estimates of existing stock and the expected life of the equipment as a basis for calculating

the portion of historical shipments that replaced existing stock. With estimates of historical 

stock, total shipments, and replacement shipments, DOE developed a stock accounting model to 

track equipment stocks through time.

To estimate historical shipments for new installations, DOE subtracted the estimated

replacement sales from historical total sales. DOE then combined the estimated historical sales 

for new installations with indexes of historical and forecasted construction activity in seven 

building types to estimate future shipments for new installations. DOE forecasted new 

installations as a function of new construction of floor space in seven building types: health care,

lodging, foodservice, retail, education, food sales, and office.

DOE then applied this stock accounting model across the 2018–2047 forecast period by 

adding new shipments and replacement shipments, and accounting for depreciated stock that was 

replaced or demolished (i.e., removed permanently from the stock). In doing so, DOE projected 

sales of new and replacement equipment by the year sold for the forecast period. In the final step, 

DOE allocated sales in each year to the 13 primary and 12 secondary equipment classes in 

proportion to their relative historical sales.

a

9.2.1 Shipments Model Equations

DOE formulated the shipments model equations to update the stock from year t to year 

t+1 as a function of age a. 

As discussed in chapter 8 of this final rule technical support document (TSD), DOE 

estimates that the average lifetime of a unit is 8.5 years. DOE assumes that all units receive

normal repairs that do not affect the lifetime of the equipment. The stock of age a in a given year 

t is represented by:

U(t,a)

Eq. 9.1

a Primary equipment classes were modeled explicitly in all final rule analyses. Shipments were estimated for all 25 

equipment classes previously proposed for energy conservation standards. 

9-2

 
Where:

U(t,a) = stock of age a in a given year t,

a = age of stock (year), and

t = year.

The shipments of new stock in a given year is Uship(t). By definition, the age of the

equipment is zero in the year that it is shipped, so that Uship(t) = U(t,0).

9.2.2 Stock Events

In the transition from year t to year t +1, three things could happen to the stock of

automatic commercial ice makers:

1. existing equipment could fail, or be removed during a building renovation and be 

replaced,

2. existing equipment could be retired permanently, generally in a building demolition 

or renovation during which the business type(s) situated in the building changes, or

3. the stock could simply age by 1 year.

In the shipments model, DOE did not consider early replacements (i.e., existing 

equipment that is replaced before it fails) and assumed all failed equipment was replaced. The 

following sections present the equations used to represent each possible event.

9.2.2.1 Replacing Equipment

DOE determined the probability that ACIM equipment of age a from stock U0 will fail or 

will be replaced using a Weibull survival distribution function. The Weibull function describes a 

series of probabilities of equipment failure in given years. The function was developed for year 0 

through year 16. Equipment failures started exhibiting non-zero probabilities in year 3. The

failure probability declines to zero in year 13. The distribution produces an average time of 

equipment failure of roughly 8.5 years, and results in 93 percent of stock being replaced in the 7-

to 10-year lifetime range, and nearly 99 percent replaced between 6 and 11 years. The Weibull 

function resulted in the set of failure probabilities by year shown on Table 9.2.1. To develop the 

probabilities, DOE used a Weibull function with a shape parameter set to 9.0 and a scale 

parameter set to 8.98. The distribution was estimated at 0.1-year increments with each calendar 

year set equal to an average of the values over a range of the calendar year (e.g., 9.0) plus or 

minus 0.5 years.

9-3
Table 9.2.1 Probability of Equipment Failure by Year

Years Probability

3 0.0%

4 0.2%

5 1.0%

6 4.1%

7 12.7%

8 27.7%

9 35.0%

10 17.4%

11 1.8%

12 0.0%

DOE defined the quantities of replaced equipment as follows:

UB(t,a) = PB0(a) × U0(t,a)

Eq. 9.2

Where:

UB(t,a) = quantity of units of age a in year t replaced,

PB0(a) = probability that stock of existing units of age a will fail or will be replaced,

U0(t,a) = original stock of units of age a in year t (i.e., the probability of failure is applied against 

the original new stock installed in the historical year, yielding an age a in year t, not against 

the remaining stock),

a = age of stock (years), and

t = year.

9.2.2.2 New Equipment

Available information suggests that the purchase of new equipment for use in new 

buildings is driven by the rate of new construction in proportion to new floor space. 

By definition, for each type of building:

EFS(t+1) = EFS(t) + NFS(t) - DFS(t)

Eq. 9.3

Where:

EFS(t) = existing floor space in year t (ft2),

NFS(t) = new floor space added in year t (ft2), and

DFS(t) = floor space demolished in year t (ft2).

9-4
The number of units installed in new buildings is as follows:

UN(t) = UN(t-1) × NFS(t)/ NFS(t-1)

Eq. 9.4

Where:

UN(t) = the number of units installed in new buildings in year t.

DOE has little information on the variation in the market saturation of automatic 

commercial ice makers by building type or over time. Therefore, in the model, DOE assumed 

that the purchase of new equipment is driven by the construction of new floor space, and that 

failed or removed equipment is replaced on a one-for-one basis.

9.3 DATA INPUTS

9.3.1 Historical Shipments

Historical shipments data came from two main sources. The Air-Conditioning, Heating, 

and Refrigeration Institute (AHRI) provided ACIM shipments data for 2010. The AHRI data

provided a distribution of shipments by equipment class. AHRI also provided a 10-year history 

of total shipments. Additionally, DOE collected the U.S. Census Bureau’s Current Industrial 

Report (CIR) data for the 1973 to 2009 period, including total ice maker shipments. DOE 

reviewed the total shipments in the AHRI and CIR data and noted that the CIR-reported 

shipments were consistently higher than the AHRI-reported shipments. DOE considered the 

possibility that these discrepancies were associated with net exports. DOE identified one source 

with identifiable export information, the North American Association of Food Equipment 

Manufacturers (NAFEM). NAFEM data for two relatively recent calendar years (2007 and 2008) 

showed approximately 20 percent of total ice maker shipments associated with food service 

equipment as exports. Applying a 20-percent export factor to the CIR shipments data brought the 

CIR data into approximate agreement with the AHRI data. Given the similarity between the data 

after adjusting the CIR data and given that the AHRI data are better understood than the CIR 

data, for the final rule analysis, DOE relied on the AHRI shipment values.

DOE also collected historical data from ENERGY STAR for 2008 through 2012. 

ENERGY STAR® reports the number of units shipped that meet the minimum efficiency levels 

to qualify for certification and the percentage of total shipments that are represented by the 

ENERGY STAR qualifying shipments. DOE compared the total shipments implied by the 

ENERGY STAR data to the total AHRI shipments for the equipment classes covered by 

ENERGY STAR criteria. On average, the AHRI shipments were 90.7 percent of the ENERGY 

STAR shipments over the 2008–2012 period. DOE believes the difference between the two data 

sources to be caused by a larger number of manufacturers included in the ENERGY STAR 

accounting than in the AHRI accounting. Thus, DOE decided to use the ENERGY STAR data to 

adjust the shipments forecast.

Using CIR data, DOE created a rolling estimate of total existing stock by aggregating

historical shipments across 8.5-year historical periods. Using shipments, stock, and the estimated 

9-5
8.5-year life of the equipment, DOE estimates that, on average, 14 percent of shipments were for 

new construction and the remainder for replacement of existing stock.

Next, DOE used the split between shipments to new buildings and shipments for stock 

replacement to derive new shipments using the historical AHRI shipments with the adjustment 

based on ENERGY STAR shipments as a starting point. Starting with an estimated 2010 stock 

value created by aggregating the last 8.5 years of adjusted AHRI shipments data, DOE began

adding shipments, subtracting estimated stock retirements, and developing a forecast of 

surviving stock for 2011 through 2047.

To identify appropriate building construction estimates for future growth in the forecast 

step, DOE distributed the estimated total 2010 existing ice maker stock across building types 

using distributions from a 2009 commercial refrigeration equipment report from Navigant 

Consulting, Inc.

1 Thus divided, the projections of new construction activity for each building 

type derived from National Energy Modeling System (NEMS) input files were used to project 

shipments for each building type.

b The distribution by building type is shown on Table 9.3.1. Ice 

maker shipment and stock projections were then aggregated across building types by year, and

the totals ultimately were distributed across all 25 equipment classes using distributions derived 

from the AHRI 20102 data.

Table 9.3.1 Distribution of Ice Maker Stock by Building Type

Building Type Share of Stock

Health Care 9.1%

Lodging 33.2%

Foodservice 22.3%

Retail 7.7%

Education 7.3%

Food Sales 16.2%

Office 4.2%

9.3.2 Historical Shipments and Projected Building Stock Additions

DOE converted the historical shipment data in each equipment class to percentages of 

total shipped units of automatic commercial ice makers for use in the shipments model. This

established the ACIM equipment market share attributed to each equipment class for a base, 

historical data year.

DOE developed the projections of building construction from the 2014 Annual Energy 

Outlook (AEO2014).3 Table 9.3.2 shows the forecast square footage of new construction used to 

scale annual new ACIM equipment shipments. As the data in Table 9.3.2 show, forecast squarefootage additions to the building stocks vary from year to year. Therefore, the forecast ACIM

b For some types of consumer products and commercial equipment, specific classes of equipment are often linked to 

specific types of buildings. Hence, the step of dividing the equipment stock among building types captures crucial 

information for the forecast of specific equipment classes. In the case of ACIM equipment, DOE currently has no 

specific data on such specific linkages between types of buildings and equipment classes. However, because 

building stocks for each of the seven building types are projected to grow at varying rates, projecting shipments by 

building type enables DOE to capture as much information as possible from the NEMS commercial building 

construction projections.

9-6

 
equipment shipments show some variability as well, tracking the forecast floor space additions. 

The AEO2014 projections covered the period up to 2040. DOE used the growth rates over the 

last 10 years of the AEO2014 forecast (2031–2040) to extrapolate the AEO forecast through the 

year 2047 to develop the full 30-year forecast needed for the national impacts analysis (see

chapter 10).

Table 9.3.2 AEO2014 Forecast of Commercial Floor Space Additions by Building Type

Year

New Construction of Commercial Floor Space

million ft2

Health 

Care Lodging Foodservice Retail Education Food Sales Office

2013 66 147 31 279 247 21 174

2018 67 164 51 428 209 36 411

2020 65 176 47 404 197 33 451

2025 63 181 48 444 169 34 392

2030 71 150 55 515 190 39 276

2035 72 207 57 527 228 40 415

2040 76 188 56 565 252 40 403

Annual Growth 

Factor, 2031-2040 2.4% 2.5% 2.4% 2.5% 1.7% 2.3% 2.1%

Source: U.S. Department of Energy, Energy Information Administration, Annual Energy Outlook 2014, data file kdbout.

DOE used the AHRI shipments data to derive the percentage of units shipped for each of 

the 13 primary equipment classes and 12 secondary equipment classes. Table 9.3.3 shows the 

fraction of the units shipped for each of the 25 equipment classes.

Table 9.3.3 ACIM Equipment Shipments, Percent by Equipment Class

Equipment Class* Percentage of 

Shipments Equipment Class Percentage of Shipments

IMH-W-Small-B 4.54% SCU-W-Large-B 0.22%

IMH-W-Med-B 2.90% SCU-A-Small-B 13.85%

IMH-W-Large-B** 0.00% SCU-A-Large-B 6.56%

 IMH-W-Large-B1 0.37% IMH-W-Small-C 0.68%

 IMH-W-Large-B2 0.11% IMH-W-Large-C 0.17%

IMH-A-Small-B 27.08% IMH-A-Small-C 3.53%

IMH-A-Large-B** 0.00% IMH-A-Large-C 1.07%

 IMH-A-Large-B-1 13.61% RCU-Small-C** 0.83%

 IMH-A-Large-B-2 2.53% RCU-Large-C** 0.87%

RCU-Small-B† 5.43% SCU-W-Small-C 0.15%

RCU-Large-B**,† 0.00% SCU-W-Large-C 0.00%

 RCU-Large-B-1 5.80% SCU-A-Small-C 8.75%

 RCU-Large-B-2 0.27% SCU-A-Large-C 0.00%

SCU-W-Small-B 0.68% Total 100.00%

Source: AHRI shipment data for 2010 submitted to DOE as part of this rulemaking.

* IMH: ice-making head; RCU: remote condenser unit; SCU: self-contained unit; A: Air-cooled; W: Watercooled; B: Batch; C: Continuous 

** IMH-W-Large-B, IMH-A-Large-B, and RCU-Large-B were modeled as two separate typical units rather than 

one equipment class.

† Remote compressing classes for remote condensing and without remote condensing were modeled as 

combined classes within the small/large and batch type/continuous type groupings. Thus, eight classes are shown 

9-7
on this table as four classes.

9.3.3 Building Space and Market Saturation

The amount of commercial floor space is the main driver for ACIM equipment shipments 

and is one of the basic inputs to the shipments model. The model divides commercial floor space 

into new construction floor space (NFS) and existing floor space (EFS).

For this analysis, “commercial square footage with ACIM equipment” refers to both the 

new and the existing stock of buildings.

9.3.3.1 Floor Space: New Construction

DOE used the projected floor space construction after the year 2009 from the NEMS

projection underlying AEO2014. DOE extracted annual estimates of new floor space additions 

from an AEO2014 data file (kdbout) for the 2004 through 2040 period. As stated in section 9.3.2, 

DOE used the last 10 years of the AEO forecast to develop growth rates used to extend the 

forecast to 2047.

DOE projected the shipments of ice makers for new buildings by multiplying the 

estimated shipments to new buildings in 2010 by future indices of new construction for each

building type. DOE developed the index for each building type by dividing floor space 

projections in each future year by the prior year’s floor space projection.

Future shipments for new buildings were factored into the stock accounting model. 

Future shipments to replace existing stock were developed using the Weibull function described 

in section 9.2.2.1.

9.3.3.2 Market Shipments

DOE combined the shipments for new buildings and replacement of existing stock to 

yield total shipments in year t. DOE used total shipments to estimate shipments for each ACIM

equipment class y, MKTSHIP(t,y), as shown with Eq. 9.5. Because DOE assumed that the 

percent of shipments in each class was constant over time and the lifetime of each class was the

same, the ratio of shipments of a specific equipment class indicates the market share for that 

class, MKT(y). DOE assumed the MKT(y) for a particular equipment class to be a constant value 

equal to the percent historical shipments to that class. Table 9.3.3 reports these percentages.

MMMMMMMMMMMMMM(tt, yy) = MM (yy) × (UUUU(tt) + UUUU(tt))

Eq. 9.5

Where:

MKTSHIP(t,y) = total market shipments of equipment class y in year t,

MKT(y) = percentage of total market shipments in equipment class y (%),

UN(t) = total shipments for new construction in year t, and

UB(t) = total replacement shipments in year t.

9-8
9.4 RESULTS

Table 9.4.1 shows DOE’s shipments forecast for the ACIM equipment by type of 

commercial building.

Table 9.4.2 shows DOE’s shipments forecast for the ACIM equipment classes at the 

base-case energy consumption level (or efficiency level 1) by equipment type.

Table 9.4.1 Forecast Shipments of New and Replacement Automatic Commercial Ice 

Makers by Building Type, 2018−2047 (Base Case)

Building

Sector

Number of Units (for selected years, and cumulative)

2018 2020 2025 2030 2035 2040 2045 2047 Cumulative 

(2018 to 2047)

Health Care

– New

 

2,049 

 

1,968 

 

1,905 

 

2,158 

 

2,204 

 

2,314 

 

2,490 

 

2,549 65,419

– Replacement*

 

15,696 

 

16,093 

 

18,984 

 

18,412 

 

20,431 

 

20,711 

 

22,250 

 

22,098 585,066

Lodging

– New 

7,918 

 

8,472 

 

8,741 

 

7,224 

 

9,989 

 

9,082 

 

10,458 

 

10,710 272,594

– Replacement*

 

57,258 

 

58,403 

 

68,986 

 

68,168 

 

75,772 

 

76,327 

 

84,044 

 

82,960 2,160,796

Foodservice

– New

 

9,140 

 

8,532 

 

8,633 

 

9,855 

 

10,175 

 

10,157 

 

11,314 

 

11,566 295,638

– Replacement*

 

38,597 

 

39,742 

 

49,186 

 

49,174 

 

56,864 

 

59,381 

 

66,186 

 

66,912 1,604,502

Retail

– New

 

2,304 

 

2,175 

 

2,387 

 

2,772 

 

2,837 

 

3,039 

 

3,285 

 

3,361 83,441

– Replacement*

 

13,352 

 

13,692 

 

16,368 

 

16,182 

 

18,362 

 

19,053 

 

20,953 

 

21,133 523,651

Education

– New

 

1,099 

 

1,036 

 

889 

 

1,003 

 

1,201 

 

1,327 

 

1,275 

 

1,290 33,469

– Replacement*

 

12,693 

 

12,959 

 

14,889 

 

14,244 

 

15,387 

 

15,357 

 

16,272 

 

16,031 445,968

Food Sales

– New

 

6,719 

 

6,261 

 

6,343 

 

7,266 

 

7,487 

 

7,459 

 

8,285 

 

8,459 217,209

– Replacement*

 

27,982 

 

28,832 

 

35,738 

 

35,778 

 

41,430 

 

43,332 

 

48,344 

 

48,909 1,168,632

Office

– New

 

1,970 

 

2,162 

 

1,879 

 

1,322 

 

1,988 

 

1,931 

 

2,063 

 

2,106 56,937

– Replacement*

 

7,240 

 

7,416 

 

9,208 

 

9,736 

 

10,891 

 

11,179 

 

12,723 

 

12,809 305,877

Total

– New

 

31,200 

 

30,605 

 

30,778 

 

31,599 

 

35,880 

 

35,310 

 

39,170 

 

40,041 1,024,708 

– Replacement*

 

172,818 

 

177,136 

 

213,359 

 

211,695 

 

239,136 

 

245,341 

 

270,772 

 

270,851 6,794,493 

– Total

 

204,018 

 

207,741 

 

244,137 

 

243,295 

 

275,015 

 

280,650 

 

309,943 

 

310,892 7,819,200 

* Replacement includes equipment replaced from original stock (installed before standards) and the standards-level equipment 

replaced in subsequent years.

9-9
Equipment purchase price increases associated with lower energy consumption levels 

could cause shipments to drop relative to the base case. DOE acknowledges that the shipment 

projections under the standards cases could be lower than those in the base case because the 

higher installed costs could cause some customers to forego or delay discretionary equipment 

purchases. DOE calculated an average impact factor of -0.625, -6.25 percent for a 10-percent 

price increase, based on an average of input received from seven people interviewed during 

manufacturer interviews. The difference between the shipments-weighted average total installed 

cost of equipment in the baseline condition and TSL 3 is 2.7 percent. Based on the price impact 

factor of -6.25, the impact of TSL 3 price changes is under 2 percent. DOE performed a 

sensitivity analysis on the impact of price on shipments and the national impact analysis. The 

results are reported in appendix 10B. DOE performed low or high range shipments projections as 

additional sensitivity analyses reported in appendix 10B of this final rule TSD.

Table 9.4.2 Forecast Shipments of Automatic Commercial Ice Makers, 2018−2047, by 

Equipment Type (Base Case)*

Equipment Class

Number of Units by Equipment Class (for selected years and cumulative)

2018 2020 2025 2030 2035 2040 2045 Cumulative

(2018 – 2047)

IMH-W-Small-B 9,263 9,432 11,085 11,046 12,487 12,743 14,072 355,019

IMH-W-Med-B 5,760 6,024 7,079 7,055 7,975 8,138 8,987 226,732

IMH-W-Large-B 1,074 998 1,173 1,169 1,321 1,348 1,489 37,556

 IMH-W-Large-B1 813 762 895 892 1,009 1,029 1,137 28,678

 IMH-W-Large-B2 261 236 277 276 312 319 352 8,878

IMH-A-Small-B 63,704 56,246 66,100 65,872 74,460 75,986 83,917 2,117,051

IMH-A-Large-B 39,385 33,524 39,397 39,261 44,380 45,289 50,016 1,261,806

 IMH-A-Large-B1 33,229 28,275 33,229 33,114 37,431 38,198 42,185 1,064,241

 IMH-A-Large-B2 6,157 5,249 6,169 6,147 6,949 7,091 7,831 197,565

RCU-Large-B** 14,282 12,624 14,836 14,785 16,712 17,055 18,835 475,159

 RCU-Large-B1 13,637 12,056 14,168 14,119 15,960 16,287 17,987 453,781

 RCU-Large-B2 645 568 667 665 752 767 847 21,378

SCU-W-Large-B 527 450 528 527 595 607 671 16,922

SCU-A-Small-B 35,116 28,778 33,820 33,703 38,098 38,878 42,936 1,083,185

SCU-A-Large-B 17,176 13,628 16,016 15,961 18,042 18,411 20,333 512,956

IMH-A-Small-C 9,469 7,341 8,628 8,598 9,719 9,918 10,953 276,324

IMH-A-Large-C 2,923 2,230 2,621 2,612 2,953 3,013 3,328 83,950

RCU-Small-C** 2,276 1,719 2,020 2,013 2,276 2,322 2,565 64,698

SCU-A-Small-C 24,081 18,177 21,361 21,287 24,063 24,556 27,119 684,150

Total 225,035 191,171 224,663 223,888 253,079 258,264 285,220 7,195,506

* Excludes projections for secondary equipment classes.

** Remote compressing classes for remote condensing and without remote condensing were modeled as combined classes 

within the small/large and batch type/continuous type groupings.

9-10
REFERENCES

1. Navigant Consulting, Inc. Energy Savings Potential and R&D Opportunities for 

Commercial Refrigeration. September 23, 2009. Prepared for the U.S. Department of 

Energy, Washington, D.C.

2. Air-Conditioning, Heating, and Refrigeration Institute. 2010 shipments data submittal. 

February 2011. Arlington, VA.

3. U. S. Department of Energy—Energy Information Administration. 2014 Annual Energy 

Outlook. 2014. Washington D.C. DOE/EIA-0383(2014). (Last accessed July 18, 2014.) 

<www.eia.gov/oiaf/aeo/tablebrowser/>.

9-11
CHAPTER 10. NATIONAL IMPACT ANALYSIS

TABLE OF CONTENTS

10.1 INTRODUCTION ............................................................................................................ 10-1

10.2 NATIONAL ENERGY SAVINGS .................................................................................. 10-1

10.2.1 National Energy Savings Definition ..................................................................... 10-1

10.2.2 National Energy Savings Inputs............................................................................ 10-2

10.2.2.1 Annual Unit Energy Consumption......................................................... 10-3

10.2.2.2 Shipments............................................................................................... 10-5

10.2.2.3 Equipment Stock.................................................................................... 10-5

10.2.2.4 National Annual Energy Consumption.................................................. 10-6

10.2.2.5 Site-to-Source Conversion Factor.......................................................... 10-6

10.2.2.6 Full-Fuel-Cycle Energy ......................................................................... 10-7

10.2.3 Water Consumption Inputs................................................................................... 10-8

10.3 NET PRESENT VALUE.................................................................................................. 10-9

10.3.1 Net Present Value Definition................................................................................ 10-9

10.3.2 Net Present Value Inputs .................................................................................... 10-10

10.3.2.1 Total Annual Installed Cost ................................................................. 10-10

10.3.2.2 Equipment Price Trends....................................................................... 10-12

10.3.2.3 Total Annual Operating Cost Savings ................................................. 10-14

10.3.2.4 Discount Factor.................................................................................... 10-17

10.3.2.5 Present Value of Costs......................................................................... 10-18

10.3.2.6 Present Value of Savings..................................................................... 10-18

10.4 NATIONAL ENERGY SAVINGS AND NET PRESENT VALUE RESULTS........... 10-18

10.4.1 National Energy Savings and Net Present Value Input Summary...................... 10-18

10.4.2 National Energy Savings Results........................................................................ 10-19

10.4.3 Water Impacts..................................................................................................... 10-23

10.4.4 Annual Costs and Savings .................................................................................. 10-25

10.4.5 Net Present Value Results................................................................................... 10-26

10.5 ANNUALIZED NATIONAL COSTS AND BENEFITS .............................................. 10-30

10.5.1 Calculation Method............................................................................................. 10-31

10.5.2 Results for the Adopted Standards...................................................................... 10-32

REFERENCES ........................................................................................................................ 10-34

LIST OF TABLES

Table 10.2.1 National Energy Saving Inputs............................................................................. 10-2

Table 10.2.2 Market Shares by Efficiency Level, Base Case.................................................... 10-3

Table 10.2.3 Unit Energy Consumption by Efficiency Level ................................................... 10-4

Table 10.2.4 Shipment-Weighted Average Unit Energy Consumption by Efficiency Level.... 10-5

Table 10.2.5 Projected Site-to-Source Conversion Factors....................................................... 10-7

Table 10.2.6 Full-Fuel-Cycle Energy Multipliers...................................................................... 10-7

Table 10.2.7 Unit Water Consumption by Efficiency Level ..................................................... 10-8

Table 10.2.8 Shipment-Weighted Unit Water Consumption by Efficiency Level.................... 10-9

Table 10.3.1 Net Present Value Inputs .................................................................................... 10-10

10-i
Table 10.3.2 Average Total Installed Cost per Unit by Efficiency Level ............................... 10-11

Table 10.3.3 Shipment-Weighted Average Total Installed Cost per Unit, by 

Efficiency Level............................................................................................... 10-12

Table 10.3.4 Price Trend Scenarios......................................................................................... 10-13

Table 10.3.5 Average Annual Repair Cost per Unit................................................................ 10-15

Table 10.3.6 Shipment-Weighted Annual Repair Cost per Unit ............................................. 10-16

Table 10.3.7 Average Annual Maintenance Cost per Unit...................................................... 10-17

Table 10.4.1 NES and NPV Inputs.......................................................................................... 10-19

Table 10.4.2 Cumulative Primary National Energy Savings, by EL, for Automatic 

Commercial Ice Makers Purchased in 2018-2047.......................................... 10-20

Table 10.4.3 Primary National Energy Savings, by Trial Standard Level, for Automatic 

Commercial Ice Makers Purchased in 2018-2047.......................................... 10-21

Table 10.4.4 Primary Energy TSL National Energy Savings as Percent of Base Energy Usage of 

Equipment Purchased in 2018-2047............................................................... 10-21

Table 10.4.5 Cumulative Full-Fuel-Cycle National Energy Savings, by EL, for 

Automatic Commercial Ice Makers Purchased in 2018-2047........................ 10-22

Table 10.4.6 Full-Fuel-Cycle National Energy Savings, by Trial Standard Level, for 

Automatic Commercial Ice Makers Purchased in 2018-2047........................ 10-23

Table 10.4.7 Cumulative Water Savings, by EL, for Automatic Commercial Ice Makers 

Purchased in 2018-2047.................................................................................. 10-24

Table 10.4.8 Cumulative Water Savings, by TSL, for Automatic Commercial Ice Makers 

Purchased in 2018-2047.................................................................................. 10-25

Table 10.4.9 Cumulative NPV Results, by EL, Based on a 7-Percent Discount Rate ............ 10-27

Table 10.4.10 Cumulative NPV Results, by TSL, Based on a 7-Percent Discount Rate ........ 10-28

Table 10.4.11 Cumulative NPV Results, by EL, Based on a 3-Percent Discount Rate .......... 10-29

Table 10.4.12 Cumulative NPV Results, by TSL, Based on a 3-Percent Discount Rate ........ 10-30

Table 10.5.1 Annualized Benefits and Costs of New and Amended Standards for 

Automatic Commercial Ice Makers Shipped in 2018–2047*.......................... 10-32

LIST OF FIGURES

Figure 10.4.1 National Annual Costs and Savings for Efficiency Level 4.............................. 10-26

10-ii
CHAPTER 10. NATIONAL IMPACT ANALYSIS

10.1 INTRODUCTION

This chapter describes the method used to estimate the magnitude and net present value 

(NPV) of future national energy savings (NES) from candidate efficiency levels for automatic 

commercial ice-making (ACIM) equipment energy conservation standards. Results of the 

national impact analysis (NIA) described in this chapter include national energy consumption 

and savings, monetary value of energy savings to the nation as a result of standards, increased 

total installed costs to the nation as a result of standards, and the NPV of energy savings (the 

difference between the present monetary value of energy savings and increased total installed 

costs). 

The U.S. Department of Energy (DOE) determined both the NPV and NES for each

potential standard level considered for 13 primary equipment classes of automatic commercial 

ice makers examined in this final rule. Sections 10.2 and 10.3 present the definitions and the 

inputs to NES and NPV, respectively. Section 10.4 presents the result of the NIA. DOE

performed all calculations using a Microsoft Excelspreadsheet, which is available at 

www.eere.energy.gov/buildings/appliance_standards/ commercial_products.html. Details and 

instructions for using the spreadsheet are discussed in appendix 10A of this technical support 

document (TSD).

10.2 NATIONAL ENERGY SAVINGS

10.2.1 National Energy Savings Definition

DOE calculates annual NES for a given year as the difference between two scenarios: a 

base case without new energy conservation standards and a standards case with new energy 

conservation standards as shown in Eq. 10.1. Positive values of NES correspond to net energy 

savings (i.e., national annual energy consumption (AEC) with standards is less than AEC in the 

base case).

NNNNNNtt = AA AAbbbbbbbb,tt − AA AA ssss ssss ,tt

Eq. 10.1

Where:

NESt = national energy savings in the year t (quadrillion British thermal units (quads)),

AECbase,t = annual national energy consumption in the year t in the base-case scenario (quads),

and

AECstandards,t = annual national energy consumption in the year t in a standards-case scenario 

(quads).

DOE calculates cumulative NES as the sum of annual NES from each year, starting with 

the year in which the standards that are the subject of this rulemaking become effective (2018), 

and through the end of the study period (2047). As discussed below, the NES also includes 

10-1
energy savings for equipment purchased in the final study year, and through the year in which 

the final year purchases are retired. Cumulative NES is explained by Eq. 10.2.

NNNNNNcccccc = ∑tt NNNNNNtt

Eq. 10.2

DOE calculated the AEC for base-case and standards-case scenarios by multiplying the 

number or stock of automatic commercial ice makers (by vintage) by the unit energy 

consumption (UEC) (also by vintage), as shown by Eq. 10.3:

AA AAtt = ∑ ( SSSS SSVV × UU VV VV ) × ffffff_ cccccctt

Eq. 10.3

Where:

AECt = national annual energy consumption in the year t (quads), summed over vintages of 

ACIM stock,

STOCKV = stock of automatic commercial ice makers (millions of units) of vintage V surviving 

in the year for which DOE calculated AEC (vintages range from 1 to approximately 

14 years),

UECV = annual unit energy consumption in kilowatt-hours (kWh) for equipment of vintage V,

ffc_convt = time-dependent conversion factors to convert site energy to full-fuel-cycle (FFC) 

energy by converting site energy to source energy (Btu/kWh) and adding the energy 

consumed in extracting, processing, and transporting or distributing primary fuels (calculated 

as a percent of site energy)

t = year of forecast.

The ACIM stock depends on annual shipments and equipment lifetime. DOE

acknowledges that the shipment projections under the standards cases could be lower than those 

in the base case because the higher installed costs could cause some customers to forego or delay

discretionary equipment purchases, but, as discussed in Chapter 7, DOE believes that the 

changes should be modest due to the modest increase in shipment-weighted average installed 

costs (under 3 percent for the levels chosen for the new and amended standards).

10.2.2 National Energy Savings Inputs

Table 10.2.1 lists the inputs for the determination of NES.

Table 10.2.1 National Energy Saving Inputs

Input

Annual unit energy consumption (UEC)

Shipments

Equipment stock (STOCKV)

Site-to-source conversion factor (src_conv)

10-2
10.2.2.1 Annual Unit Energy Consumption

The annual UEC is the site energy consumed by an automatic commercial ice maker in a 

given year. As described in the following paragraphs, DOE forecasted annual shipment-weighted 

average equipment efficiencies that, in turn, enabled the determination of shipment-weighted 

AEC values.

Table 10.2.2 provides estimated shipment-weighted market shares, by efficiency level, 

for each of the equipment classes analyzed in this rulemaking. The market shares by efficiency 

level for batch-type equipment classes were based on data provided by Air-Conditioning, 

Heating, and Refrigeration Institute (AHRI) in response to the notice of proposed rulemaking. 

The market shares for continuous-type equipment classes were developed from information

about available equipment obtained from published sources (see TSD chapter 3, Market and 

Technology Assessment).

Table 10.2.2 Market Shares by Efficiency Level, Base Case

Equipment Class

Market Share by Efficiency Level

Level 

1

Level 

2

Level 

3

Level 

3A

Level 

4

Level 

4A

Level 

5

Level 

6

Level 

7

IMH-W-Small-B 37.1% 15.6% 44.8% 2.5% 0.0% 0.0%

IMH-W-Med-B 55.8% 20.0% 15.3% 8.9%

IMH-W-Large-B*

 IMH-W-Large-B1 87.2% 12.8%

 IMH-W-Large-B2 87.2% 12.8%

IMH-A-Small-B 23.7% 29.5% 46.8% 0.0% 0.0% 0.0% 0.0%

IMH-A-Large-B*

 IMH-A-Large-B1 34.1% 27.8% 35.1% 0.5% 2.5% 0.0%

 IMH-A-Large-B2 16.8% 22.5% 60.8%

RCU-Large-B*

 RCU-Large-B1 43.9% 36.4% 18.8% 1.0%

 RCU-Large-B2 43.9% 36.4% 19.8%

SCU-W-Large-B 71.6% 0.6% 0.0% 22.5% 5.4% 0.0%

SCU-A-Small-B 51.8% 15.3% 12.9% 8.0% 12.0% 0.0% 0.0%

SCU-A-Large-B 62.6% 14.8% 21.5% 0.0% 1.1% 0.0%

IMH-A-Small-C 30.6% 11.1% 19.4% 5.6% 19.4% 13.9%

IMH-A-Large-C 43.5% 21.7% 17.4% 8.7% 8.7%

RCU-Small-C 27.8% 27.8% 33.3% 5.6% 0.0% 5.6%

SCU-A-Small-C 44.1% 8.8% 14.7% 17.6% 14.7% 0.0%

IMH: ice-making head; RCU: remote condenser unit; SCU: self-contained unit; A: Air cooled; W: Water cooled; 

B: Batch; C: Continuous 

* The NIA modeled the two typical sizes for IMH-W-Large-B, IMH-A-Large-B, and RCU-Large-B, rather than modeling these 

as one class.

For continuous-type equipment, DOE estimated the market shares based on the number 

of available ice maker models for each equipment class with the energy use consistent with the 

defined efficiency levels (i.e., the model has energy use no greater than its assigned efficiency 

level, but not as low as the next higher efficiency level). Using the computed breakout of 

currently available models along with AHRI shipment data, DOE created a distribution of units 

by equipment class and efficiency level.

10-3
The market share for each equipment class by efficiency level is defined as the efficiency 

level share. Because DOE had no information regarding future changes in market shares among

equipment classes, DOE assumed the market share for a particular equipment class to be 

constant over time.

Table 10.2.3 provides the annual UEC values for each efficiency level for all 13 primary

equipment classes. The annual UEC is the amount of energy used by an individual ice maker at 

each of the specified efficiency levels, in kWh/unit/year.

Table 10.2.3 Unit Energy Consumption by Efficiency Level

Equipment Class

Unit Energy Consumption by Efficiency Level

kWh/year*

,

**

Level 

1

Level 

2

Level 

3

Level 

3A

Level 

4

Level 

4A

Level 

5

Level 

6

Level 

7

IMH-W-Small-B 2,832 2,551 2,411 2,270 2,227 2,162

IMH-W-Med-B 6,040 5,439 5,138 4,951

IMH-W-Large-B 10,750 9,891

 IMH-W-Large-B1 9,166 8,405

 IMH-W-Large-B2 15,868 14,693

IMH-A-Small-B 3,535 3,184 3,009 2,901 2,833 2,658 2,640

IMH-A-Large-B 8,078 7,272 6,931 6,881 6,622 6,411

 IMH-A-Large-B1 7,350 6,617 6,251 6,192 5,885 5,636

 IMH-A-Large-B2 11,999 10,802 10,591

RCU-Large-B 12,118 10,908 10,316 10,066

 RCU-Large-B1 11,679 10,514 9,931 9,664

 RCU-Large-B2 18,671 16,807 16,077

SCU-W-Large-B 3,499 3,151 2,978 2,804 2,630 2,464

SCU-A-Small-B 2,177 1,962 1,854 1,747 1,639 1,532 1,473

SCU-A-Large-B 3,011 2,713 2,563 2,414 2,265 2,141

IMH-A-Small-C 4,299 3,872 3,658 3,445 3,231 3,201

IMH-A-Large-C 8,270 7,445 7,033 6,621 6,348

RCU-Small-C 7,737 6,966 6,580 6,195 5,809 5,688

SCU-A-Small-C 3,416 3,077 2,907 2,738 2,568 2,515

IMH: ice-making head; RCU: remote condenser unit; SCU: self-contained unit; A: Air cooled; W: Water cooled; 

B: Batch; C: Continuous 

* Blank cells indicate that insufficient design options were identified to specify additional efficiency levels. Levels 3A and 4A 

are only applicable to classes with 22-inch and non-22-inch cost efficiency curves.

** The NIA modeled the two typical sizes for IMH-W-Large-B, IMH-A-Large-B, and RCU-Large-B, rather than modeling these 

as one class. The UECs for these three classes are weighted averages of the B1 and B2 typical units, using weights provided in 

TSD chapter 7.

To estimate NES, DOE examines the total equipment stock–which is distributed across 

efficiency levels. In other words, the base-case electricity consumption is not specifically set to 

level 1, but is rather, a shipment-weighted average UEC value for the base-case efficiency 

distribution. The same holds true for standards-case distributions. To develop standards-case 

distributions, DOE used the so-called “roll-up” method of aggregating shipments. In the roll-up 

method, when a standard is contemplated that eliminates equipment below a specific efficiency 

level from the market, all shipments below the standard level roll up into the lowest efficiency 

level available under the standard. For example, as Table 10.2.2 shows, in the base case, 37

percent of IMH-W-Small-B machines were shipped at level 1 efficiency. If level 1 is eliminated, 

those shipments would roll up into efficiency level 2. For each equipment class, DOE used the 

10-4
roll-up method to calculate shipment-weighted average UEC for the efficiency distribution at

each efficiency level. Table 10.2.4 shows the resulting shipment-weighted average AEC values.

Table 10.2.4 Shipment-Weighted Average Unit Energy Consumption by Efficiency Level

Equipment Class

Shipment-Weighted Average Unit Energy Consumption

kWh/year*

Level 

1

Level 

2

Level 

3

Level 

3A

Level 

4

Level 

4A

Level 

5

Level 

6

Level 

7

IMH-W-Small-B 2,585 2,481 2,407 2,270 2,227 2,162

IMH-W-Med-B 5,685 5,349 5,121 4,951

IMH-W-Large-B** 10,640 9,891

 IMH-W-Large-B1 9,068 8,405

 IMH-W-Large-B2 15,718 14,693

IMH-A-Small-B 3,185 3,102 3,009 2,901 2,833 2,658 2,640

IMH-A-Large-B** 7,369 7,127 6,923 6,875 6,622 6,411

 IMH-A-Large-B1 6,718 6,468 6,242 6,185 5,885 5,636

 IMH-A-Large-B2 10,874 10,674 10,591

RCU-Large-B** 11,320 10,789 10,314 10,066

 RCU-Large-B1 10,907 10,396 9,928 9,664

 RCU-Large-B2 17,481 16,663 16,077

SCU-W-Large-B 3,294 3,045 2,920 2,795 2,630 2,464

SCU-A-Small-B 2,004 1,892 1,820 1,734 1,639 1,532 1,473

SCU-A-Large-B 2,863 2,676 2,560 2,412 2,265 2,141

IMH-A-Small-C 3,719 3,589 3,500 3,369 3,227 3,201

IMH-A-Large-C 7,565 7,207 6,938 6,597 6,348

RCU-Small-C 6,938 6,724 6,509 6,167 5,803 5,688

SCU-A-Small-C 3,067 2,917 2,827 2,713 2,568 2,515

IMH: ice-making head; RCU: remote condenser unit; SCU: self-contained unit; A: Air cooled; W: Water cooled; 

B: Batch; C: Continuous 

* Blank cells indicate that insufficient design options were identified to specify additional efficiency levels. Levels 3A and 4A are 

only applicable to classes with 22-inch and non-22-inch cost efficiency curves.

** The NIA modeled the two typical sizes for IMH-W-Large-B, IMH-A-Large-B, and RCU-Large-B, rather than modeling these 

as one class. The UECs for these three classes are weighted averages of the B1 and B2 typical units, using weights provided in 

TSD chapter 7.

10.2.2.2 Shipments

DOE forecasted shipments for all years in the analysis period. These results are presented 

in TSD chapter 9, Shipments Analysis. 

10.2.2.3 Equipment Stock 

The ACIM stock in a given year is the total number of units that are in use in that year.

The NES spreadsheet model keeps track of the total number of automatic commercial ice makers

shipped each year. For the NES and NPV calculations, DOE estimated that an average of 

11.8 percent of the existing automatic commercial ice makers are retired in each year (based on 

an 8-1⁄2 -year average lifetime) although the actual retirements are determined in the shipments 

model. For units shipped in 2047, DOE assumes that any units still remaining at the end of 2055

will be replaced. 

10-5
10.2.2.4 National Annual Energy Consumption

The national AEC is the product of the annual UEC and the stocks of automatic 

commercial ice makers of each vintage, as shown in Eq. 10.3.

DOE initially calculated the AEC at the site (i.e., electricity in kilowatt-hours consumed 

by the automatic commercial ice makers). DOE then calculated primary energy consumption by 

applying a conversion factor to account for losses associated with the generation, transmission, 

and distribution of electricity.

Historically, DOE reported NES in terms of primary energy expressed in quadrillion 

British thermal units, or quads. DOE has begun to also estimate FFC energy savings. 76 FR 

51282 (Aug. 18, 2011), as amended at 77 FR 49701 (Aug. 17, 2012). The FFC metric includes 

the energy consumed in extracting, processing, and transporting primary fuels, and thus presents 

a more complete picture of the impacts of energy efficiency standards. DOE’s approach is based 

on the calculation of an FFC multiplier for each of the energy types used by covered equipment. 

While DOE stated in the notice of proposed rulemaking that it intended to use the 

Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation (GREET) model to 

conduct the analysis, DOE also said it would review alternative methods, including the use of the 

DOE/Energy Information Administration (EIA)’s National Energy Modeling System (NEMS).a

NEMS is a public domain, multi-sectored, partial equilibrium model of the U.S. energy sector. 

Each year, DOE/EIA uses NEMS to produce the Annual Energy Outlook (AEO). After 

evaluating both models and the approaches discussed in the August 18, 2011 notice, DOE has 

determined NEMS is a more appropriate tool for this application. 77 FR 49701 (Aug. 17, 2012). 

Therefore, DOE is using the NEMS model to conduct FFC analyses. For the final rule analysis, 

DOE calculated FFC energy savings using a methodology described in appendix 10D.

10.2.2.5 Site-to-Source Conversion Factor 

DOE calculates primary energy savings (power plant consumption) by applying a factor 

to account for losses associated with the generation, transmission, and distribution of electricity. 

DOE derived annual average site-to-power plant factors based on the version of the NEMS that 

corresponds to EIA’s Annual Energy Outlook 2014 (AEO2014).1 The factors change over time in 

response to forecasted changes in the types of power plants projected to provide electricity to the 

nation. Table 10.2.5 shows the site-to-power plant factors from 2018 to the end of the projection 

period. For years after 2040 (the last year in the AEO), DOE held the site-to-source factor 

constant at the 2040 value.

a For more information on NEMS, refer to DOE/EIA documentation. A useful summary is National Energy 

Modeling System: An Overview 2003, DOE/EIA-0581(2003), March 2003.

10-6

 
Table 10.2.5 Projected Site-to-Source Conversion Factors

Year

Site-to-Source 

Conversion Factor

Btu/kWh

Year

Site-to-Source 

Conversion Factor 

Btu/kWh

2018 10,727 2038 8,805

2019 10,727 2039 8,821

2020 10,486 2040 8,852

2021 10,388 2041 8,852

2022 10,184 2042 8,852

2023 9,884 2043 8,852

2024 9,714 2044 8,852

2025 9,713 2045 8,852

2026 9,541 2046 8,852

2027 9,331 2047 8,852

2028 9,203 2048 8,852

2029 9,149 2049 8,852

2030 9,073 2050 8,852

2031 9,024 2051 8,852

2032 8,882 2052 8,852

2033 8,814 2053 8,852

2034 8,790 2054 8,852

2035 8,761 2055 8,852

2036 8,767 2056 8,852

2037 8,782 2057 8,852

10.2.2.6 Full-Fuel-Cycle Energy

The FFC measure includes point-of-use (site) energy, the energy losses associated with 

generation, transmission, and distribution of electricity, and the energy consumed in extracting, 

processing, and transporting or distributing primary fuels. DOE’s traditional approach 

encompasses site energy and the energy losses associated with generation, transmission, and 

distribution of electricity. To complete the full-fuel-cycle by encompassing the energy consumed 

in extracting, processing, and transporting or distributing primary fuels, which DOE refers to as 

“upstream” activities, DOE developed FFC multipliers using the data and projections generated 

by NEMS and published in AEO2014. While the AEO does not provide direct calculations of 

FFC metrics, it does provide extensive information about the energy system, including 

projections of future oil, natural gas, and coal supply, energy use for oil and gas field and 

refinery operations, and fuel consumption and emissions related to electric power production. 

This information can be used to define a set of parameters representing the energy intensity of 

energy production.

Table 10.2.6 shows the FFC energy multipliers used for automatic commercial ice 

makers for selected years. The method used to calculate a time series of FFC energy multipliers 

is described in appendix 10D.

Table 10.2.6 Full-Fuel-Cycle Energy Multipliers 

Fuel 2017 2020 2025 2030 2035 2040 to 2060

Electricity

(power plant 

energy use)

1.043 1.044 1.045 1.046 1.047 1.047

10-7
10.2.3 Water Consumption Inputs

Automatic commercial ice makers use water both for the ice and, in some equipment 

types, in the condenser for eliminating heat. In the engineering analysis, one design option was 

identified that saved electricity by reducing the amount of water used in certain types of 

equipment. Table 10.2.7 shows the water base-case water usage assumed by efficiency level.

Note that DOE did not set potable water usage limits in this final rule. The change in water 

usage results from the identification of an electricity-conserving design option that also reduces 

water consumption.

Table 10.2.8 shows the shipment-weighted water usage by efficiency level. Note that the 

shipment-weighted water usage only differs from the unit water consumption in equipment 

classes for which a design option was specified that impacts water usage.

Table 10.2.7 Unit Water Consumption by Efficiency Level

Equipment Class

Efficiency Level 

1,000 gallons/year*

Level 

1

Level 

2

Level 

3

Level 

3A

Level 

4

Level 

4A

Level 

5

Level 

6

Level 

7

IMH-W-Small-B 81 80 79 79 79 79

IMH-W-Med-B 224 224 224 224

IMH-W-Large-B** 503 503

 IMH-W-Large-B1 421 421

 IMH-W-Large-B2 767 767

IMH-A-Small-B 11 11 11 11 12 12 12

IMH-A-Large-B** 30 30 28 28 28 28 0

 IMH-A-Large-B1 27 27 24 24 24 24

 IMH-A-Large-B2 46 46 46

RCU-Large-B** 47 47 47 47

 RCU-Large-B1 46 46 46 46

 RCU-Large-B2 72 72 72

SCU-W-Large-B 74 69 69 69 69 69

SCU-A-Small-B 6 6 6 6 3 3 3

SCU-A-Large-B 9 9 9 6 6 6

IMH-A-Small-C 6 6 6 6 6 6

IMH-A-Large-C 17 17 17 17 17

RCU-Small-C 15 15 15 15 15 15

SCU-A-Small-C 4 4 4 4 4 4

IMH: ice-making head; RCU: remote condenser unit; SCU: self-contained unit; A: Air cooled; W: Water cooled; 

B: Batch; C: Continuous 

* Blank cells indicate that insufficient design options were identified to specify additional efficiency levels. Levels 3A and 4A 

are only applicable to classes with 22-inch and non-22-inch cost efficiency curves.

** The NIA modeled the two typical sizes for IMH-W-Large-B, IMH-A-Large-B, and RCU-Large-B, rather than modeling these 

as one class. The values for these three classes are weighted averages of the B1 and B2 typical units, using weights provided in 

TSD chapter 7.

10-8
Table 10.2.8 Shipment-Weighted Unit Water Consumption by Efficiency Level

Equipment Class

Efficiency Level 

1,000 gallons/year*

Level 

1

Level 

2

Level 

3

Level 

3A

Level 

4

Level 

4A

Level 

5

Level 

6

Level 

7

IMH-W-Small-B 80 79 79 79 79 79

IMH-W-Med-B 224 224 224 224

IMH-W-Large-B** 503 503

 IMH-W-Large-B1 421 421

 IMH-W-Large-B2 767 767

IMH-A-Small-B 11 11 11 11 12 12 12

IMH-A-Large-B** 29 29 28 28 28 28

 IMH-A-Large-B1 26 26 24 24 24 24

 IMH-A-Large-B2 46 46 46

RCU-Large-B** 47 47 47 47

 RCU-Large-B1 46 46 46 46

 RCU-Large-B2 72 72 72

SCU-W-Large-B 73 69 69 69 69 69

SCU-A-Small-B 5 5 5 5 3 3 3

SCU-A-Large-B 9 9 9 6 6 6

IMH-A-Small-C 6 6 6 6 6 6

IMH-A-Large-C 17 17 17 17 17

RCU-Small-C 15 15 15 15 15 15

SCU-A-Small-C 4 4 4 4 4 4

IMH: ice-making head; RCU: remote condenser unit; SCU: self-contained unit; A: Air cooled; W: Water cooled; 

B: Batch; C: Continuous 

* Blank cells indicate that water usage was not calculated for that efficiency level because insufficient design options were 

identified to specify additional efficiency levels. Levels 3A and 4A are only applicable to classes with 22-inch and non-22-inch 

cost efficiency curves.

** The NIA modeled the two typical sizes for IMH-W-Large-B, IMH-A-Large-B, and RCU-Large-B, rather than modeling these 

as one class. The values for these three classes are weighted averages of the B1 and B2 typical units, using weights provided in

TSD chapter 7.

For equipment classes with a design option, weighted-average water usage will remain 

constant as the NIA roll-up proceeds until the efficiency level with the water-saving design 

option is reached. At that point, all units roll into that efficiency level, and the water usage drops 

to the new level.

10.3 NET PRESENT VALUE

10.3.1 Net Present Value Definition

The NPV is the value in the present of a time series of costs and savings. The NPV is

given by 

NPV = PVS – PVC

Eq. 10.4

10-9
Where:

PVS = present value of operating cost savings (electricity, waterb, repair, and maintenance costs),

and

PVC = present value of increased total installed costs (equipment purchase price and installation

cost).

DOE determined the PVS and PVC according to the following expressions:

PPPP = ∑tt tt × DDDDtt

Eq. 10.5

PPPP = ∑tt TT tt × DDDDtt

Eq. 10.6

Where:

OCSt = total annual operating cost savings in the year t ($),

TICt = total annual installed cost increases in the year t ($),

DFt = discount factor for the year t, and

t = year (PVS is summed over 2018−2055, and PVC is summed over 2018−2047).

DOE determined the contribution to PVC for each year, from the effective date of the

standard (2018) to the year 2047, discounted to the year 2014. DOE determined the contribution 

to PVS for each year, from the effective date of the standard to the year when units purchased in 

2047 would be retired. DOE calculated costs and savings as the difference between a standards 

case and a base case. DOE calculated a discount factor from the discount rate and the number of 

years between the “present” (i.e., year to which the sum is being discounted) and the year in 

which the costs and savings occur. DOE calculated the NPV as the sum over time of the 

discounted net savings.

10.3.2 Net Present Value Inputs

Table 10.3.1 summarizes the inputs to the NPV calculation.

Table 10.3.1 Net Present Value Inputs

Input

Total annual installed cost (TICt)

Total annual operating cost savings (OCSt)

Discount factor

Present value of costs (PVC)

Present value of savings (PVS)

10.3.2.1 Total Annual Installed Cost 

The increase in the total annual installed cost is equal to the annual change in the per-unit

installed cost (difference between base case and standards case) multiplied by the shipments 

b DOE combined water and wastewater prices into a single value.

10-10

 
forecasted in the standards case. The total installed cost includes the manufacturer selling price, 

markups for wholesale and distribution chains, and installation costs. (See TSD chapter 8 for a 

discussion of the development of installed cost.) Table 10.3.2 shows the average total installed 

cost per unit for each of the equipment classes of automatic commercial ice makers by efficiency

level.

Table 10.3.2 Average Total Installed Cost per Unit by Efficiency Level

Equipment Class

Average Total Installed Cost per Unit

2013$*

Level 

1

Level 

2

Level 

3

Level 

3A

Level 

4

Level 

4A

Level 

5

Level 

6

Level 

7

IMH-W-Small-B 2,401 2,476 2,537 2,903 3,123 3,371

IMH-W- Med -B 4,216 4,325 4,607 4,943

IMH-W-Large-B** 6,129 6,913 

 IMH-W-Large-B1 5,004 5,747

 IMH-W-Large-B2 9,763 10,681

IMH-A-Small-B 2,442 2,539 2,655 2,695 2,865 3,324 3,331

IMH-A-Large-B** 4,154 4,336 4,430 4,435 4,711 5,068

 IMH-A-Large-B1 4,097 4,172 4,269 4,275 4,602 5,025

 IMH-A-Large-B2 4,465 5,221 5,298

RCU-Large-B** 6,303 6,423 6,879 7,206

 RCU-Large-B1 6,122 6,220 6,635 6,985

 RCU-Large-B2 8,998 9,465 10,516

SCU-W-Large-B 3,467 3,540 3,580 3,620 3,664 4,114

SCU-A-Small-B 2,759 2,799 2,822 2,845 2,918 3,000 3,416

SCU-A-Large-B 3,227 3,275 3,300 3,345 3,402 3,854

IMH-A-Small-C 6,620 6,674 6,709 6,745 7,258 7,264

IMH-A-Large-C 5,489 5,538 5,568 6,287 6,310

RCU-Small-C 5,641 5,690 5,758 5,808 6,495 6,523

SCU-A-Small-C 3,598 3,621 3,646 3,685 4,216 4,224

IMH: ice-making head; RCU: remote condenser unit; SCU: self-contained unit; A: Air cooled; W: Water cooled; 

B: Batch; C: Continuous 

* Blank cells indicate that insufficient design options were identified to specify additional efficiency levels. Levels 3A and 4A 

are only applicable to classes with 22-inch and non-22-inch cost efficiency curves.

** The NIA modeled the two typical sizes for IMH-W-Large-B, IMH-A-Large-B, and RCU-Large-B, rather than modeling these 

as one class. The values for these three classes are weighted averages of the B1 and B2 typical units, using weights provided in 

TSD chapter 7.

DOE developed base-case and standards-case energy efficiency scenarios as discussed in 

section 10.2.2.1. Using the efficiency level shipments distribution (Table 10.2.2) and the data in 

Table 10.2.4, DOE estimated the shipment-weighted installed cost. Table 10.3.3 shows the 

results. 

10-11
Table 10.3.3 Shipment-Weighted Average Total Installed Cost per Unit, by Efficiency Level

Equipment Class

Shipment-Weighted Average Total Installed Cost per Unit 

2013$*

Level 

1

Level 

2

Level 

3

Level 

3A

Level 

4

Level 

4A

Level 

5

Level 

6

Level 

7

IMH-W-Small-B 2,486 2,514 2,546 2,903 3,123 3,371

IMH-W- Med -B 4,362 4,423 4,637 4,943

IMH-W-Large-B** 6,229 6,913 

 IMH-W-Large-B1 5,099 5,747

 IMH-W-Large-B2 9,880 10,681

IMH-A-Small-B 2,570 2,593 2,655 2,695 2,865 3,324 3,331

IMH-A-Large-B** 4,340 4,382 4,437 4,442 4,711 5,068

 IMH-A-Large-B1 4,192 4,218 4,277 4,283 4,602 5,025

 IMH-A-Large-B2 5,141 5,268 5,298

RCU-Large-B** 6,463 6,516 6,882 7,206

 RCU-Large-B1 6,262 6,305 6,638 6,985

 RCU-Large-B2 9,468 9,673 10,516

SCU-W-Large-B 3,512 3,565 3,593 3,622 3,664 4,114

SCU-A-Small-B 2,799 2,820 2,835 2,854 2,918 3,000 3,416

SCU-A-Large-B 3,252 3,282 3,302 3,346 3,402 3,854

IMH-A-Small-C 6,864 6,880 6,894 6,917 7,259 7,264

IMH-A-Large-C 5,654 5,675 5,695 6,289 6,310

RCU-Small-C 5,752 5,765 5,803 5,847 6,497 6,523

SCU-A-Small-C 3,713 3,724 3,736 3,763 4,216 4,224

IMH: ice-making head; RCU: remote condenser unit; SCU: self-contained unit; A: Air cooled; W: Water cooled; 

B: Batch; C: Continuous 

* Blank cells indicate that insufficient design options were identified to specify additional efficiency levels. Levels 3A and 4A 

are only applicable to classes with 22-inch and non-22-inch cost efficiency curves.

** The NIA modeled the two typical sizes for IMH-W-Large-B, IMH-A-Large-B, and RCU-Large-B, rather than modeling these 

as one class. The values for these three classes are weighted averages of the B1 and B2 typical units, using the weights provided 

in TSD chapter 7.

The results in Table 10.3.3 depend on the forecasted annual shipments by efficiency 

level. If a standard were set at level 1, customers would select equipment from all of the 

available levels. If the standard were set at level 2, customers would select from equipment at 

level 2 or higher. As the standard levels increase, the range of available efficiency levels is 

reduced. The average shipment-weighted average cost depends on the equipment costs shown in 

Table 10.3.2 and the estimated shipments at each level.

10.3.2.2 Equipment Price Trends

In prior energy conservation standards rulemakings, DOE estimated the total installed 

costs per unit for equipment, and then assumed that costs remain constant throughout the 

analysis period. This assumption is conservative because installed costs tend to decrease over 

time. In 2011, DOE issued a notice of data availability (NODA) titled Equipment Price 

Forecasting in Energy Conservation Standards Analysis. 76 FR 9696 (Feb. 22, 2011). In the 

NODA, DOE proposed a methodology for analyzing whether equipment prices have trended 

downward in real terms. The methodology examines so-called experiential learning, wherein,

with ever-increasing experience with the production of a product, manufacturers are able to 

reduce their production costs through innovations in technology and process.

10-12
To account for increased efficiency in the ACIM manufacturing process over time, DOE 

used a price forecast methodology based on experiential learning (see appendix 8D). To project

the manufacturer selling price of a unit, DOE will multiply the selling price for the first year of 

the analysis period by a coefficient specific to the year of purchase relative to the year in which 

manufacturer selling price was originally estimated. The coefficient will account for the effects 

of experiential learning.

Table 10.3.4 summarizes four learning scenarios that DOE developed. The first scenario, 

constant prices, is consistent with the analyses DOE historically performed. In this scenario, 

prices are held constant, so the learning multiplier is 1.00. DOE developed the remaining three 

scenarios—the high, reference, and low learning scenarios—for this rulemaking from historical 

ice maker shipments and Producer Price Index data. Appendix 8D of this TSD documents the 

development of the price learning scenarios. For the final rule, DOE used the historically derived 

reference scenario. The total cost values reported in Table 10.3.2 include the effects of 

experiential learning for the period between 2013 and 2018. The other scenarios were used in 

sensitivity analyses, included in appendix 10B.

Table 10.3.4 Price Trend Scenarios

Year Constant Prices Historically Derived Price Trends

High Reference Low

2013 100.0% 100.00% 100.00% 100.00%

2014 100.0% 100.02% 99.90% 99.78%

2015 100.0% 100.05% 99.80% 99.56%

2016 100.0% 100.07% 99.71% 99.34%

2017 100.0% 100.09% 99.61% 99.14%

2018 100.0% 100.11% 99.52% 98.94%

2019 100.0% 100.13% 99.44% 98.76%

2020 100.0% 100.14% 99.37% 98.61%

2021 100.0% 100.16% 99.30% 98.45%

2022 100.0% 100.18% 99.22% 98.28%

2023 100.0% 100.20% 99.14% 98.10%

2024 100.0% 100.21% 99.06% 97.92%

2025 100.0% 100.23% 98.98% 97.75%

2026 100.0% 100.25% 98.90% 97.57%

2027 100.0% 100.27% 98.83% 97.41%

2028 100.0% 100.29% 98.75% 97.24%

2029 100.0% 100.30% 98.68% 97.09%

2030 100.0% 100.32% 98.62% 96.95%

2031 100.0% 100.33% 98.56% 96.81%

2032 100.0% 100.35% 98.49% 96.67%

2033 100.0% 100.36% 98.43% 96.53%

2034 100.0% 100.38% 98.36% 96.38%

2035 100.0% 100.39% 98.29% 96.23%

2036 100.0% 100.41% 98.22% 96.09%

2037 100.0% 100.42% 98.16% 95.94%

2038 100.0% 100.44% 98.09% 95.80%

2039 100.0% 100.45% 98.03% 95.67%

2040 100.0% 100.47% 97.97% 95.54%

2041 100.0% 100.48% 97.92% 95.42%

2042 100.0% 100.49% 97.86% 95.30%

10-13
Table 10.3.4 (cont)

Year Constant Prices Historically Derived Price Trends

High Reference Low

2043 100.0% 100.51% 97.80% 95.17%

2044 100.0% 100.52% 97.74% 95.05%

2045 100.0% 100.53% 97.69% 94.92%

2046 100.0% 100.55% 97.63% 94.79%

2047 100.0% 100.56% 97.57% 94.67%

10.3.2.3 Total Annual Operating Cost Savings

The national average annual operating cost savings equal the change in the annual

operating costs (difference between base case and standards case) per unit multiplied by the 

stock forecasted in the standards case. The annual operating cost includes electricity, water, 

repair, and maintenance costs.

Annual Electricity Cost Savings. As described in TSD chapter 8, Life-Cycle Cost and 

Payback Period Analysis, DOE calculated annual electricity costs based on average state-level 

commercial electricity prices. To calculate annual energy cost savings for a particular equipment

class in a given year, DOE first calculated the annual energy costs in each forecast year at each 

efficiency level. To calculate annual energy costs, DOE multiplied the weighted-average energy 

consumption at each efficiency level from Table 10.3.3 by the number of units in the equipment

class in each year, and then by the sales-weighted national average electricity prices for the seven

building types in chapter 8. To determine energy savings, DOE then subtracted the national 

energy costs at each efficiency level (level 2 and higher) from the national energy costs at the 

baseline level (level 1).

Annual Water Cost Savings. DOE used average state-level commercial water/wastewater 

prices to calculate water/wastewater cost savings. To calculate annual water/wastewater cost 

savings for a particular equipment class in a given year, DOE first calculated the annual 

water/wastewater costs in each forecast year at each efficiency level by multiplying the 

weighted-average consumption at each efficiency level by the number of units in the equipment 

class in each year. To determine cost savings, DOE subtracted the national water costs at each 

efficiency level (level 2 and higher) from the national water costs at the baseline level (level 1). 

Chapter 8 discusses in more detail the process of defining water/wastewater prices for each 

product class and year.

Annual Repair Costs. Table 10.3.5 shows the average repair costs per unit for the 

equipment classes analyzed and Table 10.3.6 shows the shipment-weighted annual repair costs 

per unit. The development of annual repair costs is discussed in chapter 8.

10-14
Table 10.3.5 Average Annual Repair Cost per Unit

Equipment Class

Repair Costs by Efficiency Level

2013$*

Level 

1

Level 

2

Level 

3

Level 

3A

Level 

4

Level 

4A

Level 

5

Level 

6

Level 

7

IMH-W-Small-B 45.01 46.53 46.53 46.53 46.53 46.53

IMH-W-Med-B 52.92 55.80 55.80 55.80

IMH-W-Large-B** 62.98 65.04

 IMH-W-Large-B1 57.86 59.66

 IMH-W-Large-B2 79.53 82.42

IMH-A-Small-B 47.32 50.55 50.55 50.55 50.55 50.55 50.55

IMH-A-Large-B** 55.96 59.73 61.18 61.18 61.24 61.24

 IMH-A-Large-B1 54.70 58.02 59.74 59.74 59.80 59.80

 IMH-A-Large-B2 62.76 68.98 68.98

RCU-Large-B** 62.99 65.46 69.11 69.11

 RCU-Large-B1 61.39 63.26 66.79 66.79

 RCU-Large-B2 86.85 98.34 103.73

SCU-W-Large-B 22.06 22.06 22.06 22.06 22.06 22.06

SCU-A-Small-B 37.17 38.07 39.10 40.14 41.10 43.04 43.38

SCU-A-Large-B 45.79 46.84 48.00 48.30 50.34 50.33

IMH-A-Small-C 78.20 80.27 81.65 82.37 102.43 105.53

IMH-A-Large-C 89.34 91.55 92.06 104.69 125.18

RCU-Small-C 85.48 87.67 89.47 90.58 106.75 120.24

SCU-A-Small-C 77.27 77.43 78.52 80.27 97.03 104.48

IMH: ice-making head; RCU: remote condenser unit; SCU: self-contained unit; A: Air cooled; W: Water cooled; 

B: Batch; C: Continuous 

* Blank cells indicate that insufficient design options were identified to specify additional efficiency levels. Levels 3A and 4A 

are only applicable to classes with 22-inch and non-22-inch cost efficiency curves.

** The NIA modeled the two typical sizes for IMH-W-Large-B, IMH-A-Large-B, and RCU-Large-B, rather than modeling these 

as one class. The values for these three classes are weighted averages of the B1 and B2 typical units, using the weights provided 

in TSD chapter 7.

10-15
Table 10.3.6 Shipment-Weighted Annual Repair Cost per Unit

Equipment Class

Repair Costs by Efficiency Level

2013$*

Level 

1

Level 

2

Level 

3

Level 

3A

Level 

4

Level 

4A

Level 

5

Level 

6

Level 

7

IMH-W-Small-B 45.97 46.53 46.53 46.53 46.53 46.53

IMH-W-Med-B 54.19 55.80 55.80 55.80

IMH-W-Large-B** 63.25 65.04

 IMH-W-Large-B1 58.09 59.66

 IMH-W-Large-B2 79.90 82.42

IMH-A-Small-B 49.78 50.55 50.55 50.55 50.55 50.55 50.55

IMH-A-Large-B** 59.17 60.29 61.18 61.18 61.24 61.24

 IMH-A-Large-B1 57.54 58.67 59.74 59.74 59.80 59.80

 IMH-A-Large-B2 67.94 68.98 68.98

RCU-Large-B** 65.10 66.18 69.11 69.11

 RCU-Large-B1 63.14 63.96 66.79 66.79

 RCU-Large-B2 94.36 99.41 103.73

SCU-W-Large-B 22.06 22.06 22.06 22.06 22.06 22.06

SCU-A-Small-B 38.26 38.74 39.43 40.25 41.10 43.04 43.38

SCU-A-Large-B 46.47 47.13 48.03 48.32 50.34 50.33

IMH-A-Small-C 87.84 88.47 89.05 89.49 102.86 105.53

IMH-A-Large-C 94.75 95.70 96.04 106.48 125.18

RCU-Small-C 89.63 90.24 91.24 92.22 107.50 120.24

SCU-A-Small-C 80.90 80.97 81.55 82.74 97.03 104.48

IMH: ice-making head; RCU: remote condenser unit; SCU: self-contained unit; A: Air cooled; W: Water cooled; 

B: Batch; C: Continuous 

* Blank cells indicate that insufficient design options were identified to specify additional efficiency levels. Levels 3A and 4A 

are only applicable to classes with 22-inch and non-22-inch cost efficiency curves.

** The NIA modeled the two typical sizes for IMH-W-Large-B, IMH-A-Large-B, and RCU-Large-B, rather than modeling 

these as one class. The values for these three classes are weighted averages of the B1 and B2 typical units, using the weights 

provided in TSD chapter 7.

Annual Maintenance Costs. The estimation of maintenance costs is described in chapter 

8. DOE assumed that maintenance costs do not increase with efficiency level. Table 10.3.7

shows the resulting annual maintenance costs per unit by efficiency level. Shipment-weighted 

maintenance costs would be identical to the values on Table 10.3.7, and are not shown 

separately.

10-16
Table 10.3.7 Average Annual Maintenance Cost per Unit

Equipment Class

Maintenance Costs by Efficiency Level

2013$*

Level 

1

Level 

2

Level 

3

Level 

3A

Level 

4

Level 

4A

Level 

5

Level 

6

Level 

7

IMH-W-Small-B 401 401 401 401 401 401

IMH-W- Med -B 609 609 609 609

IMH-W-Large-B** 812 812

 IMH-W-Large-B1 812 812

 IMH-W-Large-B2 812 812

IMH-A-Small-B 751 751 751 751 751 751 751

IMH-A-Large-B** 1,050 1,050 1,050 1,050 1,050 1,050

 IMH-A-Large-B1 1,050 1,050 1,050 1,050 1,050 1,050

 IMH-A-Large-B2 1,050 1,050 1,050

RCU-Large-B** 508 508 508 508

 RCU-Large-B1 508 508 508 508

 RCU-Large-B2 508 508 508

SCU-W-Large-B 609 609 609 609 609 609

SCU-A-Small-B 761 761 761 761 761 761 761

SCU-A-Large-B 1,066 1,066 1,066 1,066 1,066 1,066

IMH-A-Small-C 761 761 761 761 761 761

IMH-A-Large-C 1,066 1,066 1,066 1,066 1,066

RCU-Small-C 305 305 305 305 305 305

SCU-A-Small-C 761 761 761 761 761 761

IMH: ice-making head; RCU: remote condenser unit; SCU: self-contained unit; A: Air cooled; W: Water cooled; 

B: Batch; C: Continuous 

* Blank cells indicate that insufficient design options were identified to specify additional efficiency levels. Levels 3A and 4A 

are only applicable to classes with 22-inch and non-22-inch cost efficiency curves.

** The NIA modeled the two typical sizes for IMH-W-Large-B, IMH-A-Large-B, and RCU-Large-B, rather than modeling 

these as one class. The values for these three classes are weighted averages of the B1 and B2 typical units.

10.3.2.4 Discount Factor

DOE multiplied monetary values in future years by a discount factor to determine the 

present value of costs and savings. The discount factor (DF) is:

DDDD = 1

(1+ )�tt−ttpp�

Eq. 10.7

Where:

r = discount rate,

t = year in which the cost or savings are realized, and

tp = year in which the present value is being determined.

DOE estimated national impacts using two scenarios—a 3-percent and a 7-percent real 

discount rate—for the average real rate of return on private investment in the U.S. economy.

DOE used these discount rates in accordance with the Office of Management and Budget (OMB) 

guidance to Federal agencies on the development of regulatory analysis (OMB Circular A-4, 

September 17, 2003), and section E, “Identifying and Measuring Benefits and Costs,” therein. 

DOE defined the present year as 2014 for the final rule.

10-17
10.3.2.5 Present Value of Costs 

The present value of increased installed costs is the annual installed cost increase in each

year (i.e., the difference between the standards case and base case) discounted to 2014 and 

summed over the 2018-2047 period over which DOE is considering the installation of ACIM 

equipment.

The increase in total installed cost refers to both equipment cost and installation cost

associated with the higher energy efficiency of automatic commercial ice makers purchased in 

the standards case compared to the base case. DOE calculated annual installed costs as the 

difference in total installed cost for new equipment purchased each year, multiplied by the 

shipments in the standards case.

10.3.2.6 Present Value of Savings 

The present value of the operating cost savings is the annual operating cost savings 

(i.e., the difference between the base case and standards case) discounted to 2014, and summed 

over the period from the effective date of the standard, 2018, to the time when the last unit 

installed in 2047 is retired from service. Savings result from net decreases in total operating costs 

(including electricity, water, repair, and maintenance) associated with the automatic commercial 

ice makers purchased in the standards case compared to the base case. Total annual operating 

cost savings are the savings per unit multiplied by the number of units of each vintage surviving 

in a particular year. Equipment consumes energy and water over its entire lifetime. For example, 

for units purchased in 2045, the present value of savings includes energy and water consumed 

after 2045 until the unit is retired from service at the end of its useful life.

10.4 NATIONAL ENERGY SAVINGS AND NET PRESENT VALUE RESULTS

DOE used the NES spreadsheet model to estimate the NES and NPV associated with

various efficiency levels. Sections 10.2.2 and 10.3.2 discuss the inputs to the NES spreadsheet.

The NES spreadsheet model is available at

www1.eere.energy.gov/buildings/appliance_standards/commercial/automatic_ice_making_equip

ment.html. Appendix 10A provides details and instructions for using the model.

For this final rule analysis, DOE selected five trial standard level (TSL) groupings based 

on the NPV values calculated for each efficiency level (EL). Appendix 10C discusses the criteria 

used for selection of TSLs. The NIA results in this section are presented for ELs and TSLs. 

Appendix 10B presents results of alternative NIA model scenarios.

10.4.1 National Energy Savings and Net Present Value Input Summary

Table 10.4.1 summarizes the inputs to the NES spreadsheet model. For each input, the 

table provides a brief description of the data source.

10-18
Table 10.4.1 NES and NPV Inputs

Input Data Description

Shipments Annual shipments from shipments model (see TSD chapter 9, 

“Shipments Analysis”).

Effective date of standard 2018

Base-case efficiencies Distribution of base-case shipments by efficiency level.

Standards-case efficiencies

Distribution of shipments by efficiency level for each standards 

case. Standards case annual market shares by efficiency level 

remain constant over time for the base case and each standards 

case.

Annual energy and water 

consumption per unit

Annual weighted-average values are a function of efficiency level 

(established in the engineering analysis, TSD chapter 5).

Total installed cost per unit Annual weighted-average values of installed cost are a function of 

efficiency level (see TSD chapter 8).

Repair cost per unit Annual weighted-average values of repair costs vary with 

efficiency level (see TSD chapter 8).

Maintenance cost per linear foot Annual weighted-average values of maintenance costs are constant 

with efficiency level (see TSD chapter 8).

Escalation of electricity prices AEO2014 forecasts (to 2040) and extrapolation for 2041 and 

beyond (see TSD chapter 8).

Escalation of water prices Historic Consumer Price Index for water-related expenditures 

extrapolated out to 2057.

Electricity site-to-source 

conversion

Conversion varies yearly and is generated by DOE’s version of the 

EIA NEMS program (a time series conversion factor; includes 

electric generation, transmission, and distribution losses).

Discount rate 3 and 7 percent real.

Present year Future costs are discounted to year 2014.

10.4.2 National Energy Savings Results

The following section provides NES results for each energy consumption level

considered for the 13 primary equipment classes of automatic commercial ice makers that DOE 

has analyzed, with 3 classes represented by 2 units of sizes typical of the high and the low ends 

of the harvest capacities covered by the class. Results are cumulative for equipment purchased 

from 2018 through 2047, and results are shown as primary energy savings in quads and as fullfuel-cycle savings in quads. DOE based inputs to the NES spreadsheet model on weightedaverage values, yielding results that are discrete point values, rather than a distribution of values 

as in the life-cycle cost analysis (see discussion of uncertainty in TSD chapter 8).

Table 10.4.2 shows the primary NES results for the efficiency levels analyzed for each 

ACIM equipment class. The primary NES include energy savings at the site level, plus energy 

losses in the generation, transmission, and distribution systems. Table 10.4.3 presents primary 

energy savings by TSL.

10-19
Table 10.4.2 Cumulative Primary National Energy Savings, by EL, for Automatic 

Commercial Ice Makers Purchased in 2018-2047

Equipment Class

National Energy Savings by EL

primary energy quads*

Level 2 Level 3 Level 

3A Level 4 Level 4A Level 5 Level 6 Level 7

IMH-W-Small-B 0.002 0.004 0.007 0.008 0.009

IMH-W- Med -B 0.005 0.008 0.010

IMH-W-Large-B** 0.002

 IMH-W-Large-B1 0.001

 IMH-W-Large-B2 0.001

IMH-A-Small-B 0.011 0.023 0.037 0.046 0.069 0.071

IMH-A-Large-B** 0.019 0.035 0.039 0.058 0.075

 IMH-A-Large-B1 0.016 0.031 0.035 0.055 0.071

 IMH-A-Large-B2 0.002 0.003

RCU-Large-B** 0.015 0.029 0.037

 RCU-Large-B1 0.014 0.027 0.035

 RCU-Large-B2 0.001 0.002

SCU-W-Large-B 0.000 0.000 0.001 0.001 0.001

SCU-A-Small-B 0.007 0.012 0.018 0.024 0.032 0.036

SCU-A-Large-B 0.006 0.010 0.014 0.019 0.023

IMH-A-Small-C 0.002 0.004 0.006 0.008 0.009

IMH-A-Large-C 0.002 0.003 0.005 0.006

RCU-Small-C 0.001 0.002 0.003 0.005 0.005

SCU-A-Small-C 0.006 0.010 0.015 0.021 0.023

IMH: ice-making head; RCU: remote condenser unit; SCU: self-contained unit; A: Air cooled; W: Water cooled; B: Batch; C: 

Continuous 

* A value of 0.000 indicates NES was less than 0.0005 quads and thus rounded to 0.000. Blank cells indicate that insufficient 

design options were identified to specify additional efficiency levels. Levels 3A and 4A are only applicable to classes with 22-

inch and non-22-inch cost efficiency curves.

** The NIA modeled the two typical sizes for IMH-W-Large-B, IMH-A-Large-B, and RCU-Large-B, rather than modeling these 

as one class. The values for these three classes are the totals of the B1 and B2 typical units.

10-20
Table 10.4.3 Primary National Energy Savings, by Trial Standard Level, for Automatic 

Commercial Ice Makers Purchased in 2018-2047

Equipment Class

National Energy Savings by TSL

primary energy quads*

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH-W-Small-B 0.002 0.002 0.004 0.004 0.009 

IMH-W-Med-B 0.005 0.005 0.005 0.008 0.010 

IMH-W-Large-B** 0.000 0.000 0.000 0.000 0.002 

 IMH-W-Large-B1 0.000 0.000 0.000 0.000 0.001 

 IMH-W-Large-B2 0.000 0.000 0.000 0.000 0.001 

IMH-A-Small-B 0.011 0.023 0.037 0.037 0.071 

IMH-A-Large-B** 0.019 0.034 0.039 0.058 0.075 

 IMH-A-Large-B1 0.016 0.031 0.035 0.055 0.071 

 IMH-A-Large-B2 0.002 0.002 0.003 0.003 0.003 

RCU-Large-B** 0.015 0.015 0.015 0.029 0.037 

 RCU-Large-B1 0.014 0.014 0.014 0.027 0.035 

 RCU-Large-B2 0.001 0.001 0.001 0.001 0.002 

SCU-W-Large-B 0.000 0.001 0.001 0.001 0.001 

SCU-A-Small-B 0.007 0.018 0.024 0.032 0.036 

SCU-A-Large-B 0.006 0.014 0.019 0.023 0.023 

IMH-A-Small-C 0.002 0.004 0.006 0.006 0.009 

IMH-A-Large-C 0.002 0.002 0.003 0.003 0.006 

RCU-Small-C 0.001 0.002 0.003 0.003 0.005 

SCU-A-Small-C 0.006 0.010 0.015 0.015 0.023 

Total 0.077 0.130 0.171 0.219 0.307 

IMH: ice-making head; RCU: remote condenser unit; SCU: self-contained unit; A: Air cooled; W: 

Water cooled; B: Batch; C: Continuous 

* A value of 0.000 indicates NES was less than 0.0005 quads and thus rounded to 0.000. 

** The NIA modeled the two typical sizes for IMH-W-Large-B, IMH-A-Large-B, and RCULarge-B, rather than modeling these as one class. The values for these three classes are the totals of 

the B1 and B2 typical units.

Table 10.4.4 presents primary energy savings by TSL as a percentage of base-case energy 

usage.

Table 10.4.4 Primary Energy TSL National Energy Savings as Percent of Base Energy 

Usage of Equipment Purchased in 2018-2047

Equipment Class

Base 

Energy 

Usage 

quads

TSL Savings as Percent of Total 2018-2057 Usage

TSL1 TSL2 TSL3 TSL4 TSL5

IMH-W-Small-B 0.064 4% 4% 6% 6% 15%

IMH-W- Med -B 0.089 5% 5% 5% 9% 12%

IMH-W-Large-B** 0.028 0% 0% 0% 0% 6%

IMH-W-Large-B1 0.018 0% 0% 0% 0% 7%

IMH-W-Large-B2 0.010 0% 0% 0% 0% 6%

IMH-A-Small-B 0.467 2% 5% 8% 8% 15%

IMH-A-Large-B** 0.644 3% 5% 6% 9% 12%

IMH-A-Large-B1 0.495 3% 6% 7% 11% 14%

IMH-A-Large-B2 0.149 2% 2% 2% 2% 2%

RCU-Large-B** 0.368 4% 4% 4% 8% 10%

RCU-Large-B1 0.343 4% 4% 4% 8% 10%

RCU-Large-B2 0.026 4% 4% 4% 4% 7%

10-21
Table 10.4.4 (cont)

Equipment Class

Base 

Energy 

Usage 

quads

TSL Savings as Percent of Total 2018-2057 Usage

TSL1 TSL2 TSL3 TSL4 TSL5

SCU-W-Large-B 0.004 7% 14% 18% 23% 23%

SCU-A-Small-B 0.150 5% 12% 16% 21% 24%

SCU-A-Large-B 0.102 6% 14% 19% 23% 23%

IMH-A-Small-C 0.071 3% 5% 8% 8% 12%

IMH-A-Large-C 0.044 4% 4% 7% 7% 14%

RCU-Small-C 0.031 3% 6% 10% 10% 16%

SCU-A-Small-C 0.145 4% 7% 10% 10% 16%

Total 2.206 3% 6% 8% 10% 14%

Table 10.4.5 reports NES results for the efficiency levels and for each equipment class, 

including the FFC adjustment. FFC energy savings are the standard savings reported by DOE. 

Table 10.4.6 presents FFC NES results by TSL.

Table 10.4.5 Cumulative Full-Fuel-Cycle National Energy Savings, by EL, for Automatic 

Commercial Ice Makers Purchased in 2018-2047

Equipment Class

National Energy Savings by EL

FFC energy quads*

Level 

2

Level 

3

Level 

3A

Level 

4

Level 

4A

Level 

5

Level 

6

Level 

7

IMH-W-Small-B 0.002 0.004 0.007 0.008 0.010

IMH-W- Med -B 0.005 0.008 0.011

IMH-W-Large-B** 0.002

 IMH-W-Large-B1 0.001

 IMH-W-Large-B2 0.001

IMH-A-Small-B 0.011 0.024 0.039 0.048 0.072 0.075

IMH-A-Large-B** 0.020 0.036 0.040 0.061 0.078

 IMH-A-Large-B1 0.017 0.033 0.037 0.057 0.075

 IMH-A-Large-B2 0.003 0.004

RCU-Large-B** 0.016 0.031 0.038

 RCU-Large-B1 0.015 0.029 0.037

 RCU-Large-B2 0.001 0.002

SCU-W-Large-B 0.000 0.000 0.001 0.001 0.001

SCU-A-Small-B 0.008 0.013 0.019 0.026 0.033 0.037

SCU-A-Large-B 0.006 0.010 0.015 0.020 0.024

IMH-A-Small-C 0.002 0.004 0.006 0.009 0.009

IMH-A-Large-C 0.002 0.003 0.005 0.007

RCU-Small-C 0.001 0.002 0.003 0.005 0.005

SCU-A-Small-C 0.007 0.011 0.016 0.022 0.024

IMH: ice-making head; RCU: remote condenser unit; SCU: self-contained unit; A: Air cooled; W: Water cooled; B: Batch; 

C: Continuous 

* A value of 0.000 indicates NES was less than 0.0005 quads and thus rounded to 0.000. Blank cells indicate that 

insufficient design options were identified to specify additional efficiency levels. Levels 3A and 4A are only applicable to 

classes with 22-inch and non-22-inch cost efficiency curves.

** The NIA modeled the two typical sizes for IMH-W-Large-B, IMH-A-Large-B, and RCU-Large-B, rather than modeling 

these as one class. The values for these three classes are the totals of the B1 and B2 typical units.

10-22
Table 10.4.6 Full-Fuel-Cycle National Energy Savings, by Trial Standard Level, for 

Automatic Commercial Ice Makers Purchased in 2018-2047

Equipment Class

Standard Level*

TSL1 TSL2 TSL3 TSL4 TSL5

IMH-W-Small-B 0.002 0.002 0.004 0.004 0.010

IMH-W-Med-B 0.005 0.005 0.005 0.008 0.011

IMH-W-Large-B** 0.000 0.000 0.000 0.000 0.002

 IMH-W-Large-B1 0.000 0.000 0.000 0.000 0.001

 IMH-W-Large-B2 0.000 0.000 0.000 0.000 0.001

IMH-A-Small-B 0.011 0.024 0.039 0.039 0.075

IMH-A-Large-B** 0.020 0.035 0.040 0.061 0.078

 IMH-A-Large-B1 0.017 0.033 0.037 0.057 0.075

 IMH-A-Large-B2 0.003 0.003 0.004 0.004 0.004

RCU-Large-B** 0.016 0.016 0.016 0.030 0.038

 RCU-Large-B1 0.015 0.015 0.015 0.029 0.037

 RCU-Large-B2 0.001 0.001 0.001 0.001 0.002

SCU-W-Large-B 0.000 0.001 0.001 0.001 0.001

SCU-A-Small-B 0.008 0.019 0.026 0.033 0.037

SCU-A-Large-B 0.006 0.015 0.020 0.024 0.024

IMH-A-Small-C 0.002 0.004 0.006 0.006 0.009

IMH-A-Large-C 0.002 0.002 0.003 0.003 0.007

RCU-Small-C 0.001 0.002 0.003 0.003 0.005

SCU-A-Small-C 0.007 0.011 0.016 0.016 0.024

Total 0.081 0.136 0.179 0.229 0.321

IMH: ice-making head; RCU: remote condenser unit; SCU: self-contained unit; A: Air cooled; W: 

Water cooled; B: Batch; C: Continuous 

* A value of 0.000 indicates NES was less than 0.0005 quads and thus rounded to 0.000. 

** The NIA modeled the two typical sizes for IMH-W-Large-B, IMH-A-Large-B, and RCU-Large-B, 

rather than modeling these as one class. The values for these three classes are the totals of the B1 and 

B2 typical units.

10.4.3 Water Impacts

For the final rule, DOE analyzed one design option that resulted in changes in potable 

water usage. Table 10.4.8 presents national water impacts resulting from the analysis of this 

design option, by EL. The estimate of water savings does not imply that DOE has set a potable 

water usage standard. Rather, it reflects the impact of using a design option that is effective in 

reducing energy usage and water usage. ACIM manufacturers may elect to use other design 

options to achieve the energy consumption limits established through this rulemaking. Table 

10.4.7 presents water savings by efficiency level while Table 10.4.8 presents water savings by

TSL.

Negative water savings shown for the IMH-A-Small-B category result from the pairing of 

the 22-inch units and non-22 inch units. The 22-inch units were estimated to use less water than 

10-23
the non-22 inch units. When the curves were merged, after the max tech level of the 22-inch unit 

is reached, customers were “shifted” to the non-22 inch units, causing the apparent negative 

water savings. The negative savings do not imply that DOE modeled a design option that saved 

energy but increased water usage.

Table 10.4.7 Cumulative Water Savings, by EL, for Automatic Commercial Ice Makers

Purchased in 2018-2047

Equipment Class Water Savings by EL

million gallons*,**

Level 

2

Level 

3

Level 

3A

Level 

4

Level 

4A

Level 

5

Level 

6

Level 

7

IMH-W-Small-B 761 1,733 1,733 1,733 1,733 

IMH-W- Med -B 0 0 0

IMH-W-Large-B** 0

 IMH-W-Large-B1 0

 IMH-W-Large-B2 0

IMH-A-Small-B 0 0 0 (5,424) (5,424) (5,424)

IMH-A-Large-B** 0 12,501 12,501 11,733 11,733

 IMH-A-Large-B1 0 12,501 12,501 11,733 11,733

 IMH-A-Large-B2 0 0

RCU-Large-B** 0 0 0

 RCU-Large-B1 0 0 0

 RCU-Large-B2 0 0

SCU-W-Large-B 336 336 336 336 336

SCU-A-Small-B 0 0 0 13,580 13,580 13,580 

SCU-A-Large-B 0 0 9,388 9,388 9,388

IMH-A-Small-C 0 0 0 0 0

IMH-A-Large-C 0 0 0 0

RCU-Small-C 0 0 0 0 0

SCU-A-Small-C 0 0 0 0 0

IMH: ice-making head; RCU: remote condenser unit; SCU: self-contained unit; A: Air cooled; W: Water cooled; B: Batch; C: 

Continuous 

* A value in parentheses is a negative number. A value of 0 indicates no design options were specified that impact water usage. 

Blank cells indicate that insufficient design options were identified to specify additional efficiency levels. Levels 3A and 4A are 

only applicable to classes with 22-inch and non-22-inch cost efficiency curves.

** The NIA modeled the two typical sizes for IMH-W-Large-B, IMH-A-Large-B, and RCU-Large-B, rather than modeling these 

as one class. The values for these three classes are the totals of the B1 and B2 typical units.

10-24
Table 10.4.8 Cumulative Water Savings, by TSL, for Automatic Commercial Ice Makers

Purchased in 2018-2047

Equipment Class Water Savings by TSL

million gallons*

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH-W-Small-B 761 761 1,733 1,733 1,733

IMH-W- Med -B 0 0 0 0 0

IMH-W-Large-B** 0 0 0 0 0

 IMH-W-Large-B1 0 0 0 0 0

 IMH-W-Large-B2 0 0 0 0 0

IMH-A-Small-B 0 0 0 0 (5,424)

IMH-A-Large-B** 0 12,501 12,501 11,733 11,733

 IMH-A-Large-B1 0 12,501 12,501 11,733 11,733

 IMH-A-Large-B2 0 0 0 0 0

RCU-Large-B** 0 0 0 0 0

 RCU-Large-B1 0 0 0 0 0

 RCU-Large-B2 0 0 0 0 0

SCU-W-Large-B 336 336 336 336 336

SCU-A-Small-B 0 0 13,580 13,580 13,580

SCU-A-Large-B 0 9,388 9,388 9,388 9,388

IMH-A-Small-C 0 0 0 0 0

IMH-A-Large-C 0 0 0 0 0

RCU-Small-C 0 0 0 0 0

SCU-A-Small-C 0 0 0 0 0

Total 1,097 22,987 37,539 36,771 31,347

IMH: ice-making head; RCU: remote condenser unit; SCU: self-contained unit; A: Air cooled; W: Water 

cooled; B: Batch; C: Continuous 

* A value in parentheses is a negative number. A value of 0 indicates no design options were specified that 

impact water usage. 

** The NIA modeled the two typical sizes for IMH-W-Large-B, IMH-A-Large-B, and RCU-Large-B, rather 

than modeling these as one class. The values for these three classes are the totals of the B1 and B2 typical 

units.

10.4.4 Annual Costs and Savings

As a prelude to providing the NPVs for each standard level in each equipment class, this 

section presents the annual equipment cost (or total installed cost) increases and annual operating 

cost savings at the national level.

Figure 10.4.1 shows the changes over time of the non-discounted annual equipment price 

increases and the non-discounted operating cost savings at level 4 for an example equipment 

class (the small air-cooled ice-making head, batch (IMH-A-Small-B) equipment class). The total 

net annual impact is the discounted value of the difference between annual installed costs and 

annual operating costs at a 7-percent discount rate. The figure also shows the net annual impact, 

which is the difference between the savings and costs for each year. The annual equipment price 

change is the increase in equipment price between base and standards cases for equipment 

purchased each year over the period 2018–2047. The annual operating savings are the savings in 

operating costs for equipment purchased but not retired for each year over the time period 

2018−2055. DOE determined the annual costs and savings based on the AEO2014 reference case

electricity prices. The NPV is the difference between the cumulative annual discounted savings 

10-25
and the cumulative annual discounted costs. The NIA can produce comparable figures for all 

other efficiency levels and other equipment classes.

Figure 10.4.1 National Annual Costs and Savings for Efficiency Level 4

10.4.5 Net Present Value Results

The following section provides NPV results for the efficiency levels considered for the 

equipment classes of automatic commercial ice makers. Results are cumulative and are shown as 

the discounted value of these savings. DOE based the inputs to the NES spreadsheet model on 

weighted-average values yielding results that are discrete point values, rather than a distribution 

of values as in the life-cycle cost analysis (see TSD chapter 8). The present value of increased 

total installed costs is the total installed cost increase discounted to 2014 and summed over the 

2018–2047 period. The installed costs embodied in the results below are based on reference case 

price trends.

Savings result from decreases in operating costs associated with the higher energy and 

water efficiency of automatic commercial ice maker units purchased in the standards case 

compared to the base case. Total operating cost savings are the savings per unit, multiplied by 

the number of units of each vintage. The operating cost includes energy and water consumed and 

maintenance and repair costs incurred until all units purchased during the analysis period (2018–

2047) are retired from service. DOE based all results on the reference electricity price trend from 

AEO2014.

Table 10.4.9 shows the NPV results for the standard levels considered for automatic 

commercial ice makers based on a 7-percent discount rate. Table 10.4.10 presents cumulative 

NPV results by TSL based on a 7-percent discount rate.

Product Class: IMH-A-Small-B

Efficiency Level 4 at a 7% Discount Rate

-15.000

-10.000

-5.000

0.000

5.000

10.000

15.000

20.000

Millions 2013$

Year

Net Savings

Undiscounted Annual Equipment Costs Savings

Undiscounted Total O&M Cost Savings

Discounted Total Net Annual Impact

10-26
Table 10.4.9 Cumulative NPV Results, by EL, Based on a 7-Percent Discount Rate

Equipment Class

Cumulative NPV by EL at 7% 

billion 2013$ *

,

**

Level 2 Level 3 Level 

3A Level 4 Level 4A Level 5 Level 6 Level 7

IMH-W-Small-B 0.006 0.011 (0.015) (0.036) (0.049)

IMH-W- Med -B 0.010 0.006 (0.008)

IMH-W-Large-B** (0.002)

 IMH-W-Large-B1 (0.002)

 IMH-W-Large-B2 (0.000)

IMH-A-Small-B 0.017 0.017 0.036 (0.012) (0.241) (0.238)

IMH-A-Large-B** 0.043 0.110 0.120 0.109 0.021 

 IMH-A-Large-B1 0.043 0.109 0.119 0.107 0.020 

 IMH-A-Large-B2 (0.000) 0.001 

RCU-Large-B** 0.042 0.032 0.007 

 RCU-Large-B1 0.040 0.033 0.008 

 RCU-Large-B2 0.002 (0.001)

SCU-W-Large-B 0.002 0.002 0.002 0.003 0.001

SCU-A-Small-B 0.016 0.026 0.037 0.076 0.068 (0.060)

SCU-A-Large-B 0.014 0.022 0.059 0.064 0.004

IMH-A-Small-C 0.006 0.009 0.014 (0.014) (0.014)

IMH-A-Large-C 0.005 0.009 (0.002) (0.001)

RCU-Small-C 0.002 0.004 0.008 (0.002) (0.003)

SCU-A-Small-C 0.018 0.027 0.036 (0.058) (0.062)

* A value in parentheses is a negative number. A value of 0.000 indicates NPV was less than 0.0005 billion 2013$ and thus 

rounded to 0.000. Blank cells indicate there were not enough design options identified to specify additional efficiency levels. 

Levels 3A and 4A are only applicable to classes with 22-inch and non-22-inch cost efficiency curves.

** The NIA modeled the two typical sizes for IMH-W-Large-B, IMH-A-Large-B, and RCU-Large-B, rather than modeling these 

as one class. The values for these three classes are the totals of the B1 and B2 typical units.

10-27
Table 10.4.10 Cumulative NPV Results, by TSL, Based on a 7-Percent Discount Rate

Equipment Class Cumulative NPV by TSL at 7%

billions 2013$*

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH-W-Small-B 0.006 0.006 0.011 0.011 (0.049)

IMH-W- Med -B 0.010 0.010 0.010 0.006 (0.008)

IMH-W-Large-B** 0.000 0.000 0.000 0.000 (0.002)

 IMH-W-Large-B1 0.000 0.000 0.000 0.000 (0.002)

 IMH-W-Large-B2 0.000 0.000 0.000 0.000 (0.000)

IMH-A-Small-B 0.017 0.017 0.036 0.036 (0.238)

IMH-A-Large-B** 0.043 0.109 0.120 0.109 0.021 

 IMH-A-Large-B1 0.043 0.109 0.119 0.107 0.020 

 IMH-A-Large-B2 (0.000) (0.000) 0.001 0.001 0.001 

RCU-Large-B** 0.042 0.042 0.042 0.035 0.007 

 RCU-Large-B1 0.040 0.040 0.040 0.033 0.008 

 RCU-Large-B2 0.002 0.002 0.002 0.002 (0.001)

SCU-W-Large-B 0.002 0.002 0.003 0.001 0.001 

SCU-A-Small-B 0.016 0.037 0.076 0.068 (0.060)

SCU-A-Large-B 0.014 0.059 0.064 0.004 0.004 

IMH-A-Small-C 0.006 0.009 0.014 0.014 (0.014)

IMH-A-Large-C 0.005 0.005 0.009 0.009 (0.001)

RCU-Small-C 0.002 0.004 0.008 0.008 (0.003)

SCU-A-Small-C 0.018 0.027 0.036 0.036 (0.062)

Total 0.183 0.328 0.430 0.337 (0.406)

IMH: ice-making head; RCU: remote condenser unit; SCU: self-contained unit; A: Air cooled; W: Water 

cooled; B: Batch; C: Continuous 

* A value in parentheses is a negative number. A value of 0.000 indicates NPV was less than 0.0005 billion 

2013$ and thus rounded to 0.000. 

** The NIA modeled the two typical sizes for IMH-W-Large-B, IMH-A-Large-B, and RCU-Large-B, rather 

than modeling these as one class. The values for these three classes are the totals of the B1 and B2 typical units.

Table 10.4.11 provides the NPV results based on the 3-percent discount rate and 

electricity price forecasts from the AEO2014 reference case. Table 10.4.12 presents the NPV 

results, by TSL, based on the 3-percent discount rate.

10-28
Table 10.4.11 Cumulative NPV Results, by EL, Based on a 3-Percent Discount Rate

Equipment Class

Cumulative NPV by EL at 3%

billion 2013$*

Level 2 Level 3 Level 

3A Level 4 Level 4A Level 5 Level 6 Level 7

IMH-W-Small-B 0.014 0.025 (0.019) (0.058) (0.074)

IMH-W- Med -B 0.022 0.016 (0.008)

IMH-W-Large-B** (0.003)

 IMH-W-Large-B1 (0.003)

 IMH-W-Large-B2 (0.000)

IMH-A-Small-B 0.039 0.046 0.092 0.037 (0.368) (0.360)

IMH-A-Large-B** 0.091 0.237 0.259 0.271 0.122

 IMH-A-Large-B1 0.090 0.233 0.254 0.266 0.117

 IMH-A-Large-B2 0.001 0.005

RCU-Large-B** 0.088 0.079 0.039

 RCU-Large-B1 0.084 0.080 0.039

 RCU-Large-B2 0.004 (0.001)

SCU-W-Large-B 0.003 0.004 0.005 0.005 0.002

SCU-A-Small-B 0.035 0.055 0.079 0.169 0.159 (0.075)

SCU-A-Large-B 0.030 0.047 0.127 0.138 0.031

IMH-A-Small-C 0.012 0.019 0.030 (0.021) (0.022)

IMH-A-Large-C 0.011 0.019 (0.001) 0.001

RCU-Small-C 0.005 0.009 0.017 (0.001) (0.002)

SCU-A-Small-C 0.038 0.057 0.076 (0.096) (0.103)

* A value in parentheses is a negative number. A value of 0.000 indicates NPV was less than 0.0005 billion 2013$ and 

thus rounded to 0.000. Blank cells indicate there were not enough design options identified to specify additional efficiency 

levels. Levels 3A and 4A are only applicable to classes with 22-inch and non-22-inch cost efficiency curves.

** The NIA modeled the two typical sizes for IMH-W-Large-B, IMH-A-Large-B, and RCU-Large-B, rather than 

modeling these as one class. The values for these three classes are the totals of the B1 and B2 typical units.

10-29
Table 10.4.12 Cumulative NPV Results, by TSL, Based on a 3-Percent Discount Rate

Equipment Class

Cumulative NPV by TSL at 7%

billions 2013$*

TSL1 TSL2 TSL3 TSL4 TSL5

IMH-W-Small-B 0.014 0.014 0.025 0.025 (0.074)

IMH-W-Med-B 0.022 0.022 0.022 0.016 (0.008)

IMH-W-Large-B** 0.000 0.000 0.000 0.000 (0.003)

 IMH-W-Large-B1 0.000 0.000 0.000 0.000 (0.003)

 IMH-W-Large-B2 0.000 0.000 0.000 0.000 (0.000)

IMH-A-Small-B 0.039 0.046 0.092 0.092 (0.360)

IMH-A-Large-B** 0.091 0.234 0.259 0.271 0.122

 IMH-A-Large-B1 0.090 0.233 0.254 0.266 0.117

 IMH-A-Large-B2 0.001 0.001 0.005 0.005 0.005

RCU-Large-B** 0.088 0.088 0.088 0.084 0.039

 RCU-Large-B1 0.084 0.084 0.084 0.080 0.039

 RCU-Large-B2 0.004 0.004 0.004 0.004 (0.001)

SCU-W-Large-B 0.003 0.005 0.005 0.002 0.002

SCU-A-Small-B 0.035 0.079 0.169 0.159 (0.075)

SCU-A-Large-B 0.030 0.127 0.138 0.031 0.031

IMH-A-Small-C 0.012 0.019 0.030 0.030 (0.022)

IMH-A-Large-C 0.011 0.011 0.019 0.019 0.001

RCU-Small-C 0.005 0.009 0.017 0.017 (0.002)

SCU-A-Small-C 0.038 0.057 0.076 0.076 (0.103)

Total 0.389 0.712 0.942 0.822 (0.453)

IMH: ice-making head; RCU: remote condenser unit; SCU: self-contained unit; A: Air cooled; 

W: Water cooled; B: Batch; C: Continuous 

* A value in parentheses is a negative number. A value of 0.000 indicates NPV was less than 0.0005 

billion 2013$ and thus rounded to 0.000. 

** The NIA modeled the two typical sizes for IMH-W-Large-B, IMH-A-Large-B, and RCU-Large-B, 

rather than modeling these as one class. The values for these three classes are the totals of the B1 and 

B2 typical units.

10.5 ANNUALIZED NATIONAL COSTS AND BENEFITS

The benefits and costs of the standards, for equipment sold in 2018–2047, can be 

expressed in terms of annualized values. The annualized monetary values are the sum of (1) the 

annualized national economic value of the benefits from customer operation of equipment that 

meet the proposed standards (consisting primarily of operating cost savings from using less 

energy, minus increases in equipment purchase and installation costs, which is another way of 

representing customer NPV) and (2) the annualized monetary value of the benefits of emission 

reductions, including carbon dioxide (CO2) emission reductions. The derivation of the monetary 

value of the benefits of emission reductions is described in chapter 14 of this TSD. The value of 

the CO2 reductions, otherwise known as the Social Cost of Carbon (SCC), is calculated using a 

range of values per metric ton of CO2 and developed by a recent interagency process. The 

derivation of the time series of SCC values is discussed in appendix 14A of this TSD.

10-30
Although combining the values of operating savings and CO2 reductions provides a 

useful perspective, two issues should be considered. First, the national operating cost savings are 

domestic U.S. customer monetary savings that occur as a result of market transactions, while the 

value of CO2 reductions is based on a global value. Second, the assessments of operating cost 

savings and CO2 savings are performed with different methods that use quite different time 

frames for analysis. The national operating cost savings is measured for the lifetime of 

equipment shipped in the 30-year analysis period. The SCC values, on the other hand, reflect the 

present value of future climate-related impacts resulting from the emission of 1 ton of CO2 in 

each year. These impacts go well beyond 2100.

10.5.1 Calculation Method

DOE uses a two-step calculation process to convert each time series of costs and benefits 

into annualized values. First, DOE calculates a present value in the “present” year used in 

discounting the NPV of total customer costs and savings.c For this calculation, DOE uses 

discount rates of 3 and 7 percent for all costs and benefits except for the value of CO2 reductions. 

For the latter, DOE uses the discount rate appropriate for each SCC time series (see TSD chapter 

14 for discussion).

PPPPxx = � (xx(tt) ∙ (1 + rrxx)yyNNNNNN−tt)

tt=yy1,yyTT

Eq. 10.1

Where:

xx(tt)= time series under evaluation,

PPPPxx= present value of the time series xx,

yy1= first year in the analysis period,

yyT = last year in the analysis period,

yyNPV= year to which the NPV of customers’ costs and savings are discounted, and

rrxx = discount rate used to discount the annual values of time series xx to year yyNPV.

In the second step, DOE calculates, from the present values, the fixed annual payments 

over a 30-year period, starting in the first year of the analysis period (i.e., the compliance year), 

which yields the same present values with discount rates of 3 and 7 percent. This requires 

projecting the present values in the “present” year ahead to the compliance year. The fixed 

annual payments are the annualized values.

AA xx, = PPPPxx ∙ ffyy1−yyNNNNNN, ∙ aa30, = PPPPxx ∙ (1 + rr)yy1−yyNNNNNN ∙

rr ∙ (1 + rr)30

(1 + rr)30 − 1

Eq. 10.2

Where:

AA xx, = annualized value of the time series xx,

c For the value of emissions reductions, DOE uses a time-series that corresponds to the time period used in 

calculating the operating cost savings (i.e., through the final year in which equipment shipped are still operating).

10-31

 
ff , = factor to project a value years aheadd with rr discount rate, and

aa30, = factor to annualize present values over a 30-year period with rr discount rate.

Although DOE calculates annualized values, this does not imply that the time series of 

cost and benefits from which the annualized values were determined would be a steady stream of 

payments.

10.5.2 Results for the Adopted Standards

The final rule associated with this TSD states that DOE is adopting amended energy 

conservation standards for automatic commercial ice makers that correspond to TSL 3. Estimates 

of annualized values for the proposed standards are shown in Table 10.5.1.

Table 10.5.1 Annualized Benefits and Costs of New and Amended Standards for Automatic 

Commercial Ice Makers Shipped in 2018–2047*

Discount Rate

Primary 

Estimate

Low

Estimate

High

Estimate

Monetized

million 2013$/year

Benefits

Operating Cost Savings 7% 65 62 68

3% 75 71 80

CO2 Reduction at 5% dr, average** 5% 6 6 6

CO2 Reduction at 3% dr, average** 3% 20 20 21

CO2 Reduction at 2.5% dr, average** 2.5% 29 28 30

CO2 Reduction at 3% dr, 95th perc** 3% 62 60 64

NOX Reduction at $2,684/ton** 7% 1.19 1.16 1.22

3% 1.33 1.29 1.36

Total (Operating Cost Savings, CO2

Reduction and NOX Reduction)†

7% plus CO2

range

72 to 128 69 to 123 76 to 133

7% 86 82 90

3% plus CO2

range

83 to 139 79 to 133 88 to 146

3% 97 92 102

Costs

Incremental Equipment Costs 7% 22 23 21

3% 23 24 22

Net Benefits/Costs

Total (Operating Cost Savings, CO2

Reduction and NOX Reduction, Minus 

Incremental Equipment Costs)†

7% plus CO2

range

50 to 106 46 to 100 54 to 112

7% 64 60 69

3% plus CO2

range

60 to 116 55 to 109 66 to 123

3% 74 68 80

* This table presents the annualized costs and benefits associated with automatic commercial ice makers shipped in 2018–2047. 

These results include benefits to customers that accrue after 2047 from the automatic commercial ice makers purchased from 

2018 through 2047. Costs incurred by manufacturers, some of which may be incurred before 2018 in preparation for the rule, are 

not directly included but are indirectly included as part of incremental equipment costs. In addition, incremental equipment costs 

reflect a medium decline rate for projected product price trends in the Primary Estimate, a low decline rate for projected 

equipment price trends in the Low Benefits Estimate, and a high decline rate for projected equipment price trends in the High

d

n is the number of years between the “present” year and the compliance year.

10-32

 
Benefits Estimate. The methods used to derive projected price trends are explained in appendix 10B. Operating costs reflect the 

AEO reference case electric prices in the primary estimate and the AEO low and high electric prices in the low and high 

estimates, respectively. The primary estimate reflects the a shipments projection based on AEO reference case commercial floor 

space growth, while the high and low cases represent cases are based on the AEO High and Low Economics cases. The three 

cases also include reference, high, and low water prices. Water price projections are described in TSD chapter 8 while the 

complete set of projections is contained in TSD appendix 10B.

** The CO2 values represent global monetized values (in 2013$) of the social cost of CO2 emissions in 2015 under several 

scenarios. The values of $12, $40.5, and $62.4 per metric ton (t) are the averages of SCC distributions calculated using 5-percent, 

3-percent, and 2.5-percent discount rates, respectively. The value of $119/t represents the 95th percentile of the SCC distribution 

calculated using a 3-percent discount rate. The value for NOx (in 2013$) is $2,684 per ton.

† Total benefits for both the 3-percent and 7-percent cases are derived using the SCC value calculated at a 3-percent discount 

rate, which is $40.5/t in 2015 (in 2013$). In the rows labeled as “7% plus CO2 range” and “3% plus CO2 range,” the operating 

cost and NOx benefits are calculated using the labeled discount rate, and those values are added to the full range of CO2 values.

Appendix 10E of this TSD presents detailed Regulatory Information Service Center 

(RISC) & Office of Information and Regulatory Affairs, OMB (OIRA) Consolidated Information 

System (ROCIS) tables with annualized benefits and costs by equipment class grouping for all 

TSLs considered in this final rule.

10-33
REFERENCES

1. U. S. Department of Energy—Energy Information Administration. 2014 Annual Energy 

Outlook. 2014. Washington D.C. DOE/EIA-0383(2014). (Last accessed May 14, 2014.) 

<www.eia.gov/oiaf/aeo/tablebrowser/>

10-34
APPENDIX 10A. USER INSTRUCTIONS FOR NIA SPREADSHEET

TABLE OF CONTENTS

10A.1 INTRODUCTION ...................................................................................................... 10A-1

10A.2 USER INSTRUCTIONS FOR NIA SPREADSHEET............................................... 10A-1

REFERENCES ....................................................................................................................... 10A-6

LIST OF TABLES

Table 10A.2.1 Description of Tabs in the NIA Spreadsheet .................................................. 10A-1

10A-i
APPENDIX 10A. USER INSTRUCTIONS FOR NIA SPREADSHEET

10A.1 INTRODUCTION

The results obtained in this analysis can be examined and reproduced using a Microsoft 

Excel spreadsheet available on the U.S. Department of Energy (DOE) Building Technologies 

website at www1.eere.energy.gov/buildings/appliance_standards/rulemaking.aspx/ruleid/29.

The spreadsheet enables the user to perform the DOE’s national impact analysis (NIA) 

for 12 equipment classes of automatic commercial ice makers. The spreadsheet was designed and 

tested using Excel 2010.

10A.2 USER INSTRUCTIONS FOR NIA SPREADSHEET

The NIA spreadsheet performs calculations to forecast the change in national energy use 

due to energy efficiency standards for automatic commercial ice-making (ACIM) equipment, and 

the net present value of the change in energy usage.

a The energy usage and associated costs for a 

given standard are determined first by calculating the shipments and then by calculating the 

energy and resulting costs for ACIM equipment shipped under that standard. The differences 

between the standards and base cases can then be compared and the overall energy savings and 

present values determined.

The NIA spreadsheet or workbook contains the worksheets listed in Table 10A.2.1. Some 

of the worksheets are hidden to make it easier for users to find and examine final results quickly. 

Hidden worksheets can be retrieved in Excel 2010 by right-clicking on any worksheet tab,

selecting the “Unhide” option, and then choosing a specific worksheet to unhide from the list of 

hidden worksheets.

Table 10A.2.1 Description of Tabs in the NIA Spreadsheet

Worksheet Description

Instructions Provides a description of the NIA spreadsheet.

Labels Used as a go-between for user inputs and the rest of the worksheets. Modifying this worksheet 

will change and may impair the overall operation of the model, so DOE recommends caution 

when modifying this worksheet.

Shipments 

Summary

Contains the input selections and a summary table of shipments and stock across the entire 

analysis period for the selected equipment class.

Shares Contains market shares of units by equipment class, efficiency level, and building type.

Stock Contains the accounting for equipment stocks for the complete analysis period.

Econ Trends Contains electricity and water price forecasts and experiential (price) learning data inputs.

LCC Input Contains data derived in the life-cycle cost (LCC) analysis spreadsheet. Some data are replicated 

in the NIA model to allow variations defined explicitly in the NIA.

NIA Compiled 

Data

Contains data from several other worksheets that have been organized in a way that allows for 

discounting and output table creation.

TSD Tables Contains output tables to present in the technical support document (TSD).

for NIAplus Data compiled from a macro into the format needed for the NIA plus model. 

NIAplusInputs Compiles energy savings, operating costs, and equipment costs for the NIA plus emissions 

analysis. 

a Note that the NIA model also forecasts impacts arising from changing water usage.

10A-1

 
Table 10A.2.1 (cont)

Worksheet Description

Installed Costs Contains data summarizing the full range of aggregate costs associated with installation. Uses perunit installation costs from the LCC Inputs worksheet along with shipment data.

Annual 

Operating Costs

Contains data summed from the Annual Repair & Maint, Annual Water Cost, and Annual Elec 

Cost worksheets.

Annual Repair & 

Maint

Contains maintenance and repair cost data summed from the maintenance costs worksheet and 

either the 3% or 7% repair cost worksheets, depending on the user-defined discount rate. Repair 

3% and 7% contain the same levelized costs. The existence of the two different input streams is a 

legacy that has been since been superseded. Selecting one or the other will not change the results.

Repair Costs 3% Contains repair cost data using stock data and data imported from the LCC on a per-unit basis and 

used for the 3% discount rate cases. Values are the same as used in the 7% discount case.

Repair Costs 7% Contains repair cost data using stock data and data imported from the LCC on a per-unit basis and 

used for the 7% discount rate cases. Values are the same as used in the 3% discount case.

Maintenance 

Costs

Contains maintenance cost data using stock data and data imported from the LCC on a per-unit 

basis.

Annual Water 

Cost

Contains aggregated water cost data using inputs from the LCC Inputs worksheet and stock data.

Annual Elec Cost Contains aggregated electricity cost data using inputs from the LCC Inputs worksheet and stock 

data.

Electricity Use Contains aggregated water usage data using inputs from the LCC Input worksheet and stock data.

Water Use Contains aggregated electricity usage data using inputs from the LCC Input worksheet and stock 

data.

Full Shipments 

Output

Contains the full set of shipments data for all years, efficiency levels, and equipment classes.

Full Affected 

Stock Output

Contains the full set of stock data for all years, efficiency levels, and equipment classes.

MIA Shipments Contains shipment data in the format needed to input into the manufacturer impact analysis.

Historical 

Shipments

Contains past shipments data by year and equipment class. 

Lifetime 

Distribution

Contains a Weibull lifetime distribution used in determining probabilities of survival and 

breakdown by year.

Total Stock and

Floor Space

Contains the summation of data from the seven building floor space worksheets.

Healthcare Contains historical and projected floor space data from the Annual Energy Outlook 20141

(AEO2014) to account for growth rates in healthcare buildings.

Lodging Contains historical and projected floor space data from the AEO2014 to account for growth rates 

in lodging buildings.

Food Service Contains historical and projected floor space data from the AEO2014 to account for growth rates 

in foodservice buildings.

Retail Contains historical and projected floor space data from the AEO2014 to account for growth rates 

in retail buildings.

Education Contains historical and projected floor space data from the AEO2014 to account for growth rates 

in education buildings.

Food Sales Contains historical and projected floor space data from the AEO2014 to account for growth rates 

in food sales buildings.

Office Contains historical and projected floor space data from the AEO2014 to account for growth rates 

in office buildings.

ShipmentWeighted Data

Contains output tables to present in the technical support document.

Appendix Charts Contains output charts of detailed results.

ROCIS Contains a set of tables with cumulative and annualized values for the Office of Management and 

Budget (OMB) 

10A-2
Basic instructions for using the NIA spreadsheet are as follows:

1) Once the NIA spreadsheet file has been downloaded from the DOE Building 

Technologies website, open the file using Excel. If you receive a dialog box that asks 

whether you want to enable macros, select “Yes.” At the bottom of the Excel 

window, click on the worksheet tab for the Shipments Summary worksheet.

2) Use Excel’s View/Zoom commands at the top menu bar to size the display to your 

monitor.

3) Change the user-definable model parameters in the Shipments Parameters box by first 

clicking on a parameter and then choosing a different parameter value from the 

resulting drop-down menu. The parameters are as follows:

a) Discount Year: Selects a year to which all monetary values are discounted.

b) Start Yr: 1st Yr of Stnd: This is the year in which standards are assumed to go into 

effect. The final rule modeling was based on 2018, but users can select other start 

years. Note, if a year other than 2018 is chosen, the user must import inputs from 

the LCC model with a matching start year.

c) Equipment Class: Selects the desired equipment class.

d) Efficiency Level: Selects the desired efficiency level. The selected efficiency 

level must be less than or equal to the maximum achievable technology (maxtech) level. If a higher standard level than max tech is selected, no results will be 

available for the levels above max tech.

e) Market Scenario: Not an option for the notice of proposed rulemaking phase. The 

default is “Rollup.”

f) Electricity Price Scenario: Selects the desired growth level (Constant, Reference, 

Low, or High). The scenarios refer to the Energy Information Administration’s 

(EIA’s) AEO2014 scenarios.

g) Water Price Scenario: Selects the desired growth level (reference, low, or high).

h) Discount Rate: Selects the desired discount rate (0, 3, or 7 percent).

i) Price Learning Scenario: Selects the desired growth level (no real price change, 

NEMS Price Deflator, low, reference, or high). 

j) Base Year Stock Scenario: Selects the desired base stock scenario, as follows:

i) 4-Yr Ave Current Industrial Review

ii) AHRI Shipments - 8.5 year life all eq classes

iii) AHRI Shipments - Continuous 7-yr life

iv) AHRI Shipments - Batch Eq Only @ 8.5 yr life

10A-3
k) Full-Fuel Cycle: Selects the national energy savings (full-fuel-cycle savings or 

primary savings without FFC). 

l) ENERGY STAR shipments adjustment: Yes or No, will either adjust shipments 

upward by approximately 9 percent, or not.

m) Price Impact on Shipments: Options include No Impact, an impact referred to as 

Short-Term Impact which incorporates a 6.25 percent decrease per 10 percent 

increase in average TSL 3 shipment-weighted installed cost, or, an impact 

referred to as a Long-Term Impact which attempts to temper the price impact due 

to an assumption that such impacts diminish with each year that passes since the 

price change.

n) Analysis Period - Number of Yrs: the standard analysis period is 30 year, but 

options were set up for the 9-year analysis period analyzed for the Office of 

Management and Budget, and for a 28-year period.

4) Once the user-defined parameters have been set, a macro must be run to compile all 

of the stock and shipments data for each equipment class, efficiency level, and year.

To run the macro, click the “Build Shipment/Stock Tables” button. Please note that 

output values are not correctly updated until the Build Shipment/Stock Tables macro

has been run. All compiled shipments data will be output to tables in the Full 

Shipments Output worksheet. All compiled stock data will be output to tables in the 

Full Affected Stock Output worksheet.

a) If the “AHRI Shipments - Continuous 7-yr life” is chosen, use for the Base Year 

Stock Scenario, the Batch Equipment at 8.5 and Continuous Equipment at 7 Year 

Lifetime macro button.

b) All other Base Year Stock Scenarios assume an 8.5 year lifetime for all equipment 

classes, and require use of the All Equipment, 8.5 Year Lifetime macro button.

5) If a new set of LCC results is to be loaded, they must be loaded into the LCC Input

worksheet. To do this:

a) Open the LCC workbook and execute as desired (see appendix 8A).

b) Once results are compiled in the NIA Output worksheet of the LCC workbook, 

click the “Copy All” button at the top of the page.

This will copy the exact range that is compatible with the NIA model. 

c) Once the range has been copied, switch back to the NIA workbook, in the LCC 

Input worksheet, and click the “Paste LCC Input” button to paste the inputs into 

the correct location. 

The formulas automatically update throughout the model and present results by 

equipment class and year in the NIA Compiled Data worksheet.

10A-4
6) Results are compiled further, into tables, in the Shipment-Weighted Data worksheet. 

Although many of the tables are formula-based, and will automatically update with a 

change in the user-defined parameters, some are reliant on a macro to update table 

values. To update the table values:

a) Click the “Build Tables” button located at the top right portion of the ShipmentWeighted Data worksheet.

10A-5
REFERENCES

1. U.S. Department of Energy–Energy Information Administration. Annual Energy Outlook 

2014. 2014. Washington D.C. DOE/EIA-0383(2014).

10A-6
APPENDIX 10B. NATIONAL NET PRESENT VALUE USING ALTERNATIVE

SENSITIVITY ANALYSES

TABLE OF CONTENTS

10B.1 ALTERNATIVE NET PRESENT VALUES............................................................. 10B-1

10B.1.1 Price Learning Sensitivity Analyses............................................................10B-1

10B.1.2 Electric and Water Price Sensitivity Analyses.............................................10B-9

10B.1.3 Nine-Year Sensitivity Analysis..................................................................10B-16

10B.1.4 Shipment Price Sensitivity.........................................................................10B-18

LIST OF TABLES

Table 10B.1.1 Price Factor Indices Used in Default Case and Sensitivity Cases ...................10B-3

Table 10B.1.2 Automatic Commercial Ice Makers: Net Present Value in Billions (2013$)

at a 7-Percent Discount Rate – High Price Scenario.......................................10B-4

Table 10B.1.3 Automatic Commercial Ice Makers: Net Present Value in Billions (2013$)

at a 3-Percent Discount Rate – High Price Scenario.......................................10B-5

Table 10B.1.4 Automatic Commercial Ice Makers: Net Present Value in Billions (2013$)

at a 7-Percent Discount Rate – Low Price Scenario .......................................10B-6

Table 10B.1.5 Automatic Commercial Ice Makers: Net Present Value in Billions (2013$)

at a 3-Percent Discount Rate – Low Price Scenario .......................................10B-7

Table 10B.1.6 Automatic Commercial Ice Makers: Net Present Value in Billions (2013$)

at a 7-Percent Discount Rate – Constant Real Price Scenario........................10B-8

Table 10B.1.7 Automatic Commercial Ice Makers: Net Present Value in Billions (2013$)

at a 3-Percent Discount Rate – Constant Real Price Scenario........................10B-9

Table 10B.1.8 Commercial Water Price Projections.............................................................10B-10

Table 10B.1.9 Automatic Commercial Ice Makers: Net Present Value in Billions (2013$)

at a 7-Percent Discount Rate – High Electric and Water Price Scenario......10B-11

Table 10B.1.10 Automatic Commercial Ice Makers: Net Present Value in Billions (2013$)

at a 3-Percent Discount Rate – High Electric and Water Price Scenario......10B-12

Table 10B.1.11 Automatic Commercial Ice Makers: Net Present Value in Billions (2013$)

at a 7-Percent Discount Rate – Low Electric and Water Price Scenario ......10B-13

Table 10B.1.12 Automatic Commercial Ice Makers: Net Present Value in Billions (2013$)

at a 3-Percent Discount Rate – Low Electric and Water Price Scenario ......10B-14

Table 10B.1.13 Automatic Commercial Ice Makers: Net Present Value in Billions (2013$)

at a 7-Percent Discount Rate – Constant Electric and Water Price Scenario10B-15

Table 10B.1.14 Automatic Commercial Ice Makers: Net Present Value in Billions (2013$) at 

a 3-Percent Discount Rate – Constant Electric and Water Price Scenario ....10B-16

Table 10B.1.15 Automatic Commercial Ice Makers: Net Present Value at a 7-percent 

Discount Rate for 9-Year Analysis Period for Equipment Purchased in 

2018–2026......................................................................................................10B-17

Table 10B.1.16 Automatic Commercial Ice Makers: Net Present Value at a 3-percent 

Discount Rate for 9-Year Analysis Period for Equipment Purchased in 

2018–2026......................................................................................................10B-18

10B-i
Table 10B.1.17 Automatic Commercial Ice Makers: Net Present Value in Billions (2013$)

at a 7-Percent Discount Rate – Equipment Price Elasticity Scenario...........10B-19

Table 10B.1.18 Automatic Commercial Ice Makers: Net Present Value in Billions (2013$)

at a 3-Percent Discount Rate – Equipment Price Elasticity Scenario...........10B-20

Table 10B.1.19 Automatic Commercial Ice Makers: Cumulative Energy Savings –

Equipment Price Elasticity Scenario..............................................................10B-21

LIST OF FIGURES

Figure 10B.1.1 Automatic Commercial Ice Makers Price Factor Indices for Default Case 

and Sensitivity Cases .......................................................................................10B-2

10B-ii
APPENDIX 10B. NATIONAL NET PRESENT VALUE USING 

ALTERNATIVE SENSITIVITY ANALYSES

10B.1 ALTERNATIVE NET PRESENT VALUES 

10B.1.1 Price Learning Sensitivity Analyses 

The net present value (NPV) results presented in chapter 10 of this technical support 

document (TSD) reflect a price trend based on an experience curve derived using historical data 

on shipments and refrigeration equipment producer price index (PPI) that most closely reflects 

automatic commercial ice-making (ACIM) equipment. The average annual rate of price decline 

in the default case for the 2018–2047 analysis period is 0.07 percent and is based on historical 

PPI data for refrigeration equipment in the 1978–2013 period as discussed in appendix 8D of this 

TSD. For the national impact analysis (NIA), the U.S. Department of Energy (DOE) analyzed 

two additional sensitivity cases that also use a price trend based on an exponential-in-time 

extrapolation of refrigeration equipment PPI data. DOE developed a high price decline case and 

a low price decline case in this analysis. The high price decline case is based on the upper end of 

the 95 percent confidence interval for an exponential fit to the inflation-adjusted PPI series of

1978 through 2013. The low price forecast case is based on the lower end of the 95 percent 

confidence interval for an exponential fit to the nominal PPI series of 1978 through 2013. The 

average annual rate of price decline over the analysis period is 0.15 percent in the low price 

forecast case. In the high price forecast, the average annual rate of change is an increase of 0.02

percent. Because of the nature of the exponential experience curve, greater annual price declines 

are found in earlier years in the analysis period, and lesser annual price declines are found in the 

latter years in the analysis period. DOE investigated the impact of these different product price 

forecasts on the consumer net NPV for the considered trial standard levels (TSLs) for automatic

commercial ice makers. 

For the NPV sensitivity, DOE considered three product price forecast sensitivity cases: 

(1) a high price case based on the PPI trend of 1978–2013; (2) a low price case based on the PPI 

trend in 1978–2013; and (3) a constant real price case. Each price scenario is expressed in terms 

of a price factor index time series, which is applied to the 2013 price estimate to forecast per unit 

prices over the 2018–2047 analysis period. Figure 10B.1.1 shows the equipment price factor 

indices for the default case and the three additional price decline forecasts considered in this 

sensitivity analysis. Table 10B.1.1 shows the price factor indices tabulated.

Table 10B.1.2 and Table 10B.1.3 provide NPV results for automatic commercial ice 

makers at each TSL based on the high price forecast case for 7-percent and 3-percent discount 

rates, respectively. Table 10B.1.4 and Table 10B.1.5 provide NPV results for automatic 

commercial ice makers based on the low price forecast case at 7-percent and 3-percent discount 

rates, respectively. Table 10B.1.6 and Table 10B.1.7 provide NPV results for ice makers based 

on a constant real price case at 7-percent and 3-percent discount rates, respectively. These results 

can be directly compared with the ACIM NPV results using the default price decline scenario 

shown in chapter 10 of this TSD.

10B-1
Figure 10B.1.1 Automatic Commercial Ice Makers Price Factor Indices for Default Case 

and Sensitivity Cases

0.910

0.920

0.930

0.940

0.950

0.960

0.970

0.980

0.990

1.000

1.010

1.020

2013 2017 2021 2025 2029 2033 2037 2041 2045

Price Factor Indices

Year

Low Price Decline

High Price Decline

Reference PPI Price Index

Constant Price

10B-2
Table 10B.1.1 Price Factor Indices Used in Default Case and Sensitivity Cases

Year

Price Factor Index

2013$

Default High Price 

Forecast

Low Price 

Forecast Constant Real

2013 1.000 1.000 1.000 1.0000

2014 0.999 1.000 0.998 1.0000

2015 0.998 1.000 0.996 1.0000

2016 0.997 1.001 0.993 1.0000

2017 0.996 1.001 0.991 1.0000

2018 0.995 1.001 0.989 1.0000

2019 0.994 1.001 0.988 1.0000

2020 0.994 1.001 0.986 1.0000

2021 0.993 1.002 0.984 1.0000

2022 0.992 1.002 0.983 1.0000

2023 0.991 1.002 0.981 1.0000

2024 0.991 1.002 0.979 1.0000

2025 0.990 1.002 0.977 1.0000

2026 0.989 1.003 0.976 1.0000

2027 0.988 1.003 0.974 1.0000

2028 0.988 1.003 0.972 1.0000

2029 0.987 1.003 0.971 1.0000

2030 0.986 1.003 0.970 1.0000

2031 0.986 1.003 0.968 1.0000

2032 0.985 1.003 0.967 1.0000

2033 0.984 1.004 0.965 1.0000

2034 0.984 1.004 0.964 1.0000

2035 0.983 1.004 0.962 1.0000

2036 0.982 1.004 0.961 1.0000

2037 0.982 1.004 0.959 1.0000

2038 0.981 1.004 0.958 1.0000

2039 0.980 1.005 0.957 1.0000

2040 0.980 1.005 0.955 1.0000

2041 0.979 1.005 0.954 1.0000

2042 0.979 1.005 0.953 1.0000

2043 0.978 1.005 0.952 1.0000

2044 0.977 1.005 0.950 1.0000

2045 0.977 1.005 0.949 1.0000

2046 0.976 1.005 0.948 1.0000

2047 0.976 1.006 0.947 1.0000

10B-3
Table 10B.1.2 Automatic Commercial Ice Makers: Net Present Value in Billions (2013$) at 

a 7-Percent Discount Rate – High Price Scenario

Equipment Class* Cumulative NPV by TSL at 7%

billions 2013$**

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH-W-Small-B 0.006 0.006 0.011 0.011 (0.050)

IMH-W- Med -B 0.010 0.010 0.010 0.005 (0.009)

IMH-W-Large-B† 0.000 0.000 0.000 0.000 (0.003)

 IMH-W-Large-B1 0.000 0.000 0.000 0.000 (0.002)

 IMH-W-Large-B2 0.000 0.000 0.000 0.000 (0.000)

IMH-A-Small-B 0.017 0.016 0.035 0.035 (0.243)

IMH-A-Large-B† 0.043 0.108 0.120 0.107 0.018 

 IMH-A-Large-B1 0.043 0.108 0.119 0.106 0.017 

 IMH-A-Large-B2 (0.000) (0.000) 0.001 0.001 0.001 

RCU-Large-B† 0.042 0.042 0.042 0.035 0.006 

 RCU-Large-B1 0.040 0.040 0.040 0.033 0.007 

 RCU-Large-B2 0.002 0.002 0.002 0.002 (0.001)

SCU-W-Large-B 0.002 0.002 0.003 0.001 0.001 

SCU-A-Small-B 0.016 0.036 0.076 0.067 (0.063)

SCU-A-Large-B 0.014 0.059 0.063 0.002 0.002 

IMH-A-Small-C 0.006 0.009 0.014 0.014 (0.015)

IMH-A-Large-C 0.005 0.005 0.009 0.009 (0.001)

RCU-Small-C 0.002 0.004 0.008 0.008 (0.003)

SCU-A-Small-C 0.018 0.027 0.036 0.036 (0.064)

Total 0.182 0.326 0.427 0.331 (0.424)

* IMH: ice-making head; RCU: remote condenser unit; SCU: self-contained unit; A: Air cooled; 

W: Water cooled; B: Batch; C: Continuous 

** A value in parentheses is a negative number. A value of 0.000 indicates NPV was less than 

0.0005 billion 2013$ and thus rounded to 0.000.

† The NIA modeled the two typical sizes for IMH-W-Large-B, IMH-A-Large-B, and RCU-Large-B, 

rather than modeling these as one class. The values for these three classes are the totals of the B-1 

and B-2 typical units.

10B-4
Table 10B.1.3 Automatic Commercial Ice Makers: Net Present Value in Billions (2013$) at 

a 3-Percent Discount Rate – High Price Scenario

Equipment Class* Cumulative NPV by TSL at 3%

billions 2013$**

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH-W-Small-B 0.014 0.014 0.025 0.025 (0.076)

IMH-W- Med -B 0.022 0.022 0.022 0.015 (0.009)

IMH-W-Large-B† 0.000 0.000 0.000 0.000 (0.004)

 IMH-W-Large-B1 0.000 0.000 0.000 0.000 (0.003)

 IMH-W-Large-B2 0.000 0.000 0.000 0.000 (0.000)

IMH-A-Small-B 0.039 0.044 0.090 0.090 (0.372)

IMH-A-Large-B† 0.091 0.233 0.258 0.268 0.116 

 IMH-A-Large-B1 0.090 0.232 0.253 0.264 0.111 

 IMH-A-Large-B2 0.001 0.001 0.004 0.004 0.004 

RCU-Large-B† 0.088 0.088 0.088 0.083 0.035 

 RCU-Large-B1 0.084 0.084 0.084 0.079 0.036 

 RCU-Large-B2 0.004 0.004 0.004 0.004 (0.001)

SCU-W-Large-B 0.003 0.005 0.005 0.002 0.002 

SCU-A-Small-B 0.035 0.079 0.168 0.156 (0.081)

SCU-A-Large-B 0.030 0.127 0.138 0.028 0.028 

IMH-A-Small-C 0.012 0.019 0.030 0.030 (0.023)

IMH-A-Large-C 0.011 0.011 0.019 0.019 0.001 

RCU-Small-C 0.005 0.009 0.016 0.016 (0.003)

SCU-A-Small-C 0.038 0.057 0.076 0.076 (0.106)

Total 0.387 0.708 0.935 0.810 (0.491)

* IMH: ice-making head; RCU: remote condenser unit; SCU: self-contained unit; A: Air cooled; 

W: Water cooled; B: Batch; C: Continuous 

** A value in parentheses is a negative number. A value of 0.000 indicates NPV was less than 

0.0005 billion 2013$ and thus rounded to 0.000. 

† The NIA modeled the two typical sizes for IMH-W-Large-B, IMH-A-Large-B, and RCU-Large-B, 

rather than modeling these as one class. The values for these three classes are the totals of the B-1 

and B-2 typical units.

10B-5
Table 10B.1.4 Automatic Commercial Ice Makers: Net Present Value in Billions (2013$) at 

a 7-Percent Discount Rate – Low Price Scenario

Equipment Class* Cumulative NPV by TSL at 7%

billions 2013$**

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH-W-Small-B 0.006 0.006 0.011 0.011 (0.048)

IMH-W- Med -B 0.010 0.010 0.010 0.006 (0.008)

IMH-W-Large-B† 0.000 0.000 0.000 0.000 (0.002)

 IMH-W-Large-B1 0.000 0.000 0.000 0.000 (0.002)

 IMH-W-Large-B2 0.000 0.000 0.000 0.000 (0.000)

IMH-A-Small-B 0.018 0.017 0.038 0.038 (0.233)

IMH-A-Large-B† 0.043 0.109 0.121 0.110 0.024 

 IMH-A-Large-B1 0.043 0.109 0.120 0.108 0.023 

 IMH-A-Large-B2 (0.000) (0.000) 0.001 0.001 0.001 

RCU-Large-B† 0.042 0.042 0.042 0.036 0.009 

 RCU-Large-B1 0.040 0.040 0.040 0.034 0.010 

 RCU-Large-B2 0.002 0.002 0.002 0.002 (0.001)

SCU-W-Large-B 0.002 0.002 0.003 0.001 0.001 

SCU-A-Small-B 0.017 0.037 0.077 0.069 (0.057)

SCU-A-Large-B 0.014 0.059 0.064 0.005 0.005 

IMH-A-Small-C 0.006 0.009 0.014 0.014 (0.014)

IMH-A-Large-C 0.005 0.005 0.009 0.009 (0.001)

RCU-Small-C 0.002 0.004 0.008 0.008 (0.003)

SCU-A-Small-C 0.018 0.027 0.036 0.036 (0.060)

Total 0.184 0.330 0.434 0.343 (0.388)

* IMH: ice-making head; RCU: remote condenser unit; SCU: self-contained unit; A: Air cooled; 

W: Water cooled; B: Batch; C: Continuous 

** A value in parentheses is a negative number. A value of 0.000 indicates NPV was less than 

0.0005 billion 2013$ and thus rounded to 0.000. 

† The NIA modeled the two typical sizes for IMH-W-Large-B, IMH-A-Large-B, and RCU-Large-B, 

rather than modeling these as one class. The values for these three classes are the totals of the B-1 

and B-2 typical units.

10B-6
Table 10B.1.5 Automatic Commercial Ice Makers: Net Present Value in Billions (2013$) at 

a 3-Percent Discount Rate – Low Price Scenario

Equipment Class* Cumulative NPV by TSL at 3%

billions 2013$**

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH-W-Small-B 0.014 0.014 0.025 0.025 (0.072)

IMH-W- Med -B 0.022 0.022 0.022 0.016 (0.007)

IMH-W-Large-B† 0.000 0.000 0.000 0.000 (0.003)

 IMH-W-Large-B1 0.000 0.000 0.000 0.000 (0.003)

 IMH-W-Large-B2 0.000 0.000 0.000 0.000 (0.000)

IMH-A-Small-B 0.040 0.048 0.095 0.095 (0.350)

IMH-A-Large-B† 0.092 0.235 0.260 0.273 0.128 

 IMH-A-Large-B1 0.090 0.234 0.255 0.268 0.123 

 IMH-A-Large-B2 0.002 0.002 0.005 0.005 0.005 

RCU-Large-B† 0.088 0.088 0.088 0.086 0.042 

 RCU-Large-B1 0.084 0.084 0.084 0.081 0.042 

 RCU-Large-B2 0.004 0.004 0.004 0.004 (0.001)

SCU-W-Large-B 0.003 0.005 0.005 0.002 0.002 

SCU-A-Small-B 0.036 0.080 0.170 0.161 (0.069)

SCU-A-Large-B 0.030 0.128 0.139 0.034 0.034 

IMH-A-Small-C 0.012 0.019 0.030 0.030 (0.021)

IMH-A-Large-C 0.011 0.011 0.020 0.020 0.002 

RCU-Small-C 0.005 0.009 0.017 0.017 (0.002)

SCU-A-Small-C 0.038 0.057 0.077 0.077 (0.099)

Total 0.391 0.717 0.949 0.835 (0.414)

* IMH: ice-making head; RCU: remote condenser unit; SCU: self-contained unit; A: Air cooled; 

W: Water cooled; B: Batch; C: Continuous 

** A value in parentheses is a negative number. A value of 0.000 indicates NPV was less than 

0.0005 billion 2013$ and thus rounded to 0.000. 

† The NIA modeled the two typical sizes for IMH-W-Large-B, IMH-A-Large-B, and RCU-Large-B, 

rather than modeling these as one class. The values for these three classes are the totals of the B-1 

and B-2 typical units.

10B-7
Table 10B.1.6 Automatic Commercial Ice Makers: Net Present Value in Billions (2013$) at 

a 7-Percent Discount Rate – Constant Real Price Scenario

Equipment Class* Cumulative NPV by TSL at 7%

billions 2013$**

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH-W-Small-B 0.006 0.006 0.011 0.011 (0.050)

IMH-W- Med -B 0.010 0.010 0.010 0.005 (0.009)

IMH-W-Large-B† 0.000 0.000 0.000 0.000 (0.003)

 IMH-W-Large-B1 0.000 0.000 0.000 0.000 (0.002)

 IMH-W-Large-B2 0.000 0.000 0.000 0.000 (0.000)

IMH-A-Small-B 0.017 0.016 0.035 0.035 (0.242)

IMH-A-Large-B† 0.043 0.108 0.120 0.108 0.019 

 IMH-A-Large-B1 0.043 0.108 0.119 0.107 0.018 

 IMH-A-Large-B2 (0.000) (0.000) 0.001 0.001 0.001 

RCU-Large-B† 0.042 0.042 0.042 0.035 0.006 

 RCU-Large-B1 0.040 0.040 0.040 0.033 0.007 

 RCU-Large-B2 0.002 0.002 0.002 0.002 (0.001)

SCU-W-Large-B 0.002 0.002 0.003 0.001 0.001 

SCU-A-Small-B 0.016 0.036 0.076 0.067 (0.063)

SCU-A-Large-B 0.014 0.059 0.063 0.003 0.003 

IMH-A-Small-C 0.006 0.009 0.014 0.014 (0.015)

IMH-A-Large-C 0.005 0.005 0.009 0.009 (0.001)

RCU-Small-C 0.002 0.004 0.008 0.008 (0.003)

SCU-A-Small-C 0.018 0.027 0.036 0.036 (0.063)

Total 0.182 0.326 0.428 0.332 (0.421)

*IMH: ice-making head; RCU: remote condenser unit; SCU: self-contained unit; A: Air cooled; 

W: Water cooled; B: Batch; C: Continuous 

** A value in parentheses is a negative number. A value of 0.000 indicates NPV was less than 

0.0005 billion 2013$ and thus rounded to 0.000. 

† The NIA modeled the two typical sizes for IMH-W-Large-B, IMH-A-Large-B, and RCU-Large-B, 

rather than modeling these as one class. The values for these three classes are the totals of the B-1 

and B-2 typical units.

10B-8
Table 10B.1.7 Automatic Commercial Ice Makers: Net Present Value in Billions (2013$) at 

a 3-Percent Discount Rate – Constant Real Price Scenario

Equipment Class* Cumulative NPV by TSL at 3%

billions 2013$**

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH-W-Small-B 0.014 0.014 0.025 0.025 (0.076)

IMH-W- Med -B 0.022 0.022 0.022 0.015 (0.009)

IMH-W-Large-B† 0.000 0.000 0.000 0.000 (0.004)

 IMH-W-Large-B1 0.000 0.000 0.000 0.000 (0.003)

 IMH-W-Large-B2 0.000 0.000 0.000 0.000 (0.000)

IMH-A-Small-B 0.039 0.045 0.090 0.090 (0.370)

IMH-A-Large-B† 0.091 0.233 0.258 0.269 0.117 

 IMH-A-Large-B1 0.090 0.232 0.254 0.264 0.113 

 IMH-A-Large-B2 0.001 0.001 0.004 0.004 0.004 

RCU-Large-B† 0.088 0.088 0.088 0.083 0.036 

 RCU-Large-B1 0.084 0.084 0.084 0.079 0.037 

 RCU-Large-B2 0.004 0.004 0.004 0.004 (0.001)

SCU-W-Large-B 0.003 0.005 0.005 0.002 0.002 

SCU-A-Small-B 0.035 0.079 0.168 0.157 (0.080)

SCU-A-Large-B 0.030 0.127 0.138 0.029 0.029 

IMH-A-Small-C 0.012 0.019 0.030 0.030 (0.023)

IMH-A-Large-C 0.011 0.011 0.019 0.019 0.001 

RCU-Small-C 0.005 0.009 0.016 0.016 (0.003)

SCU-A-Small-C 0.038 0.057 0.076 0.076 (0.106)

Total 0.388 0.708 0.936 0.812 (0.484)

* IMH: ice-making head; RCU: remote condenser unit; SCU: self-contained unit; A: Air cooled; 

W: Water cooled; B: Batch; C: Continuous 

** A value in parentheses is a negative number. A value of 0.000 indicates NPV was less than 

0.0005 billion 2013$ and thus rounded to 0.000. 

† The NIA modeled the two typical sizes for IMH-W-Large-B, IMH-A-Large-B, and RCU-Large-B, 

rather than modeling these as one class. The values for these three classes are the totals of the B-1 

and B-2 typical units.

10B.1.2 Electric and Water Price Sensitivity Analyses 

For the NPV sensitivity, DOE considered three electric and water price forecast 

sensitivity cases: (1) a high electric and water prices case, (2) a low electric and water prices

case, and (3) a constant electric and water prices case. Table 10B.1.8 displays the high, low, and 

reference water price scenarios used in the sensitivity cases. Table 10B.1.9 and Table 10B.1.10

provide NPV results for automatic commercial ice makers at each TSL based on the high electric 

and water price forecast case for 7- and 3-percent discount rates, respectively. Table 10B.1.11

and Table 10B.1.12 provide NPV results for automatic commercial ice makers based on the low

electric and water price forecast case at 7- and 3-percent discount rates, respectively. Table 

10B.1.13 and Table 10B.1.14 provide NPV results for ice makers based on a constant electric

and water price forecast case at 7- and 3-percent discount rates, respectively. These results can 

be directly compared with the ACIM NPV results using the default electric price scenario shown 

in chapter 10 of this TSD.

10B-9
Table 10B.1.8 Commercial Water Price Projections

Year Reference Case

2013$/1000 gal

High Case

2013$/1000 gal

Low Case

2013$/1000 gal

Constant Price Case

2013$/1000 gal

2011 $6.76 $6.92 $7.03 $6.76

2012 $6.99 $7.07 $7.13 $6.99

2013 $7.23 $7.23 $7.23 $7.23

2014 $7.37 $7.39 $7.33 $7.23

2015 $7.52 $7.55 $7.43 $7.23

2016 $7.67 $7.71 $7.54 $7.23

2017 $7.82 $7.88 $7.64 $7.23

2018 $7.98 $8.06 $7.75 $7.23

2019 $8.14 $8.23 $7.86 $7.23

2020 $8.30 $8.41 $7.97 $7.23

2021 $8.46 $8.60 $8.09 $7.23

2022 $8.63 $8.79 $8.20 $7.23

2023 $8.80 $8.98 $8.31 $7.23

2024 $8.98 $9.18 $8.43 $7.23

2025 $9.16 $9.38 $8.55 $7.23

2026 $9.34 $9.58 $8.67 $7.23

2027 $9.53 $9.79 $8.79 $7.23

2028 $9.72 $10.01 $8.92 $7.23

2029 $9.91 $10.23 $9.04 $7.23

2030 $10.11 $10.45 $9.17 $7.23

2031 $10.31 $10.68 $9.30 $7.23

2032 $10.51 $10.92 $9.43 $7.23

2033 $10.72 $11.16 $9.56 $7.23

2034 $10.94 $11.40 $9.70 $7.23

2035 $11.16 $11.65 $9.84 $7.23

2036 $11.38 $11.91 $9.97 $7.23

2037 $11.60 $12.17 $10.12 $7.23

2038 $11.84 $12.43 $10.26 $7.23

2039 $12.07 $12.71 $10.40 $7.23

2040 $12.31 $12.98 $10.55 $7.23

2041 $12.56 $13.27 $10.70 $7.23

2042 $12.81 $13.56 $10.85 $7.23

2043 $13.06 $13.86 $11.00 $7.23

2044 $13.32 $14.16 $11.16 $7.23

2045 $13.59 $14.47 $11.31 $7.23

2046 $13.86 $14.79 $11.47 $7.23

2047 $14.13 $15.11 $11.64 $7.23

2048 $14.42 $15.45 $11.80 $7.23

2049 $14.70 $15.78 $11.97 $7.23

2050 $15.00 $16.13 $12.14 $7.23

2051 $15.30 $16.48 $12.31 $7.23

2052 $15.60 $16.85 $12.48 $7.23

2053 $15.91 $17.22 $12.66 $7.23

2054 $16.23 $17.59 $12.83 $7.23

2055 $16.55 $17.98 $13.02 $7.23

2056 $16.88 $18.37 $13.20 $7.23

2057 $17.22 $18.78 $13.39 $7.23

2058 $17.56 $19.19 $13.57 $7.23

2059 $17.91 $19.61 $13.77 $7.23

2060 $18.27 $20.04 $13.96 $7.23

10B-10
Table 10B.1.9 Automatic Commercial Ice Makers: Net Present Value in Billions (2013$) at 

a 7-Percent Discount Rate – High Electric and Water Price Scenario

Equipment Class* Cumulative NPV by TSL at 7%

billions 2013$**

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH-W-Small-B 0.007 0.007 0.012 0.012 (0.048)

IMH-W- Med -B 0.011 0.011 0.011 0.007 (0.007)

IMH-W-Large-B† 0.000 0.000 0.000 0.000 (0.002)

 IMH-W-Large-B1 0.000 0.000 0.000 0.000 (0.002)

 IMH-W-Large-B2 0.000 0.000 0.000 0.000 (0.000)

IMH-A-Small-B 0.019 0.019 0.040 0.040 (0.231)

IMH-A-Large-B† 0.045 0.114 0.126 0.116 0.031 

 IMH-A-Large-B1 0.045 0.114 0.124 0.115 0.029 

 IMH-A-Large-B2 0.000 0.000 0.002 0.002 0.002 

RCU-Large-B† 0.044 0.044 0.044 0.038 0.011 

 RCU-Large-B1 0.042 0.042 0.042 0.036 0.012 

 RCU-Large-B2 0.002 0.002 0.002 0.002 (0.001)

SCU-W-Large-B 0.002 0.002 0.003 0.001 0.001 

SCU-A-Small-B 0.017 0.039 0.081 0.073 (0.055)

SCU-A-Large-B 0.015 0.062 0.067 0.007 0.007 

IMH-A-Small-C 0.006 0.009 0.015 0.015 (0.013)

IMH-A-Large-C 0.006 0.006 0.010 0.010 (0.000)

RCU-Small-C 0.003 0.005 0.008 0.008 (0.002)

SCU-A-Small-C 0.019 0.028 0.038 0.038 (0.059)

Total 0.191 0.345 0.453 0.365 (0.369)

* IMH: ice-making head; RCU: remote condenser unit; SCU: self-contained unit; A: Air cooled; 

W: Water cooled; B: Batch; C: Continuous 

** A value in parentheses is a negative number. A value of 0.000 indicates NPV was less than 

0.0005 billion 2013$ and thus rounded to 0.000. 

† The NIA modeled the two typical sizes for IMH-W-Large-B, IMH-A-Large-B, and RCU-Large-B, 

rather than modeling these as one class. The values for these three classes are the totals of the B-1 

and B-2 typical units.

10B-11
Table 10B.1.10 Automatic Commercial Ice Makers: Net Present Value in Billions (2013$) 

at a 3-Percent Discount Rate – High Electric and Water Price Scenario

Equipment Class* Cumulative NPV by TSL at 3%

billions 2013$**

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH-W-Small-B 0.015 0.015 0.026 0.026 (0.071)

IMH-W- Med -B 0.024 0.024 0.024 0.018 (0.005)

IMH-W-Large-B† 0.000 0.000 0.000 0.000 (0.003)

 IMH-W-Large-B1 0.000 0.000 0.000 0.000 (0.003)

 IMH-W-Large-B2 0.000 0.000 0.000 0.000 (0.000)

IMH-A-Small-B 0.042 0.052 0.102 0.102 (0.343)

IMH-A-Large-B† 0.096 0.247 0.273 0.290 0.146 

 IMH-A-Large-B1 0.094 0.245 0.268 0.284 0.140 

 IMH-A-Large-B2 0.002 0.002 0.006 0.006 0.006 

RCU-Large-B† 0.092 0.092 0.092 0.092 0.048 

 RCU-Large-B1 0.088 0.088 0.088 0.087 0.049 

 RCU-Large-B2 0.005 0.005 0.005 0.005 (0.000)

SCU-W-Large-B 0.004 0.005 0.006 0.003 0.003 

SCU-A-Small-B 0.037 0.084 0.180 0.171 (0.061)

SCU-A-Large-B 0.031 0.134 0.146 0.040 0.040 

IMH-A-Small-C 0.012 0.020 0.032 0.032 (0.020)

IMH-A-Large-C 0.012 0.012 0.020 0.020 0.003 

RCU-Small-C 0.005 0.010 0.017 0.017 (0.001)

SCU-A-Small-C 0.040 0.060 0.080 0.080 (0.097)

Total 0.411 0.754 0.999 0.892 (0.361)

* IMH: ice-making head; RCU: remote condenser unit; SCU: self-contained unit; A: Air cooled; 

W: Water cooled; B: Batch; C: Continuous 

** A value in parentheses is a negative number. A value of 0.000 indicates NPV was less than 

0.0005 billion 2013$ and thus rounded to 0.000. 

† The NIA modeled the two typical sizes for IMH-W-Large-B, IMH-A-Large-B, and RCU-Large-B, 

rather than modeling these as one class. The values for these three classes are the totals of the B-1 

and B-2 typical units.

10B-12
Table 10B.1.11 Automatic Commercial Ice Makers: Net Present Value in Billions (2013$) 

at a 7-Percent Discount Rate – Low Electric and Water Price Scenario

Equipment Class* Cumulative NPV by TSL at 7%

billions 2013$**

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH-W-Small-B 0.006 0.006 0.010 0.010 (0.051)

IMH-W- Med -B 0.010 0.010 0.010 0.005 (0.009)

IMH-W-Large-B† 0.000 0.000 0.000 0.000 (0.003)

 IMH-W-Large-B1 0.000 0.000 0.000 0.000 (0.002)

 IMH-W-Large-B2 0.000 0.000 0.000 0.000 (0.000)

IMH-A-Small-B 0.017 0.015 0.034 0.034 (0.242)

IMH-A-Large-B† 0.041 0.102 0.113 0.100 0.012 

 IMH-A-Large-B1 0.042 0.102 0.112 0.099 0.011 

 IMH-A-Large-B2 (0.000) (0.000) 0.001 0.001 0.001 

RCU-Large-B† 0.041 0.041 0.041 0.033 0.005 

 RCU-Large-B1 0.039 0.039 0.039 0.031 0.006 

 RCU-Large-B2 0.002 0.002 0.002 0.002 (0.001)

SCU-W-Large-B 0.001 0.002 0.002 0.001 0.001 

SCU-A-Small-B 0.016 0.035 0.070 0.061 (0.067)

SCU-A-Large-B 0.014 0.055 0.059 (0.001) (0.001)

IMH-A-Small-C 0.005 0.009 0.014 0.014 (0.015)

IMH-A-Large-C 0.005 0.005 0.009 0.009 (0.002)

RCU-Small-C 0.002 0.004 0.008 0.008 (0.003)

SCU-A-Small-C 0.018 0.027 0.035 0.035 (0.064)

Total 0.177 0.311 0.406 0.309 (0.439)

* IMH: ice-making head; RCU: remote condenser unit; SCU: self-contained unit; A: Air cooled; 

W: Water cooled; B: Batch; C: Continuous 

** A value in parentheses is a negative number. A value of 0.000 indicates NPV was less than 

0.0005 billion 2013$ and thus rounded to 0.000. 

† The NIA modeled the two typical sizes for IMH-W-Large-B, IMH-A-Large-B, and RCU-Large-B, 

rather than modeling these as one class. The values for these three classes are the totals of the B-1 

and B-2 typical units.

10B-13
Table 10B.1.12 Automatic Commercial Ice Makers: Net Present Value in Billions (2013$) 

at a 3-Percent Discount Rate – Low Electric and Water Price Scenario

Equipment Class* Cumulative NPV by TSL at 3%

billions 2013$**

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH-W-Small-B 0.013 0.013 0.023 0.023 (0.077)

IMH-W- Med -B 0.021 0.021 0.021 0.014 (0.010)

IMH-W-Large-B† 0.000 0.000 0.000 0.000 (0.004)

 IMH-W-Large-B1 0.000 0.000 0.000 0.000 (0.003)

 IMH-W-Large-B2 0.000 0.000 0.000 0.000 (0.000)

IMH-A-Small-B 0.037 0.042 0.085 0.085 (0.370)

IMH-A-Large-B† 0.088 0.218 0.242 0.250 0.099 

 IMH-A-Large-B1 0.087 0.217 0.238 0.246 0.095 

 IMH-A-Large-B2 0.001 0.001 0.004 0.004 0.004 

RCU-Large-B† 0.085 0.085 0.085 0.079 0.032 

 RCU-Large-B1 0.081 0.081 0.081 0.075 0.033 

 RCU-Large-B2 0.004 0.004 0.004 0.004 (0.001)

SCU-W-Large-B 0.003 0.004 0.005 0.002 0.002 

SCU-A-Small-B 0.034 0.076 0.154 0.142 (0.092)

SCU-A-Large-B 0.029 0.117 0.127 0.019 0.019 

IMH-A-Small-C 0.011 0.018 0.029 0.029 (0.024)

IMH-A-Large-C 0.011 0.011 0.019 0.019 0.000 

RCU-Small-C 0.005 0.009 0.016 0.016 (0.003)

SCU-A-Small-C 0.037 0.055 0.074 0.074 (0.107)

Total 0.374 0.670 0.880 0.752 (0.535)

* IMH: ice-making head; RCU: remote condenser unit; SCU: self-contained unit; A: Air cooled; 

W: Water cooled; B: Batch; C: Continuous 

** A value in parentheses is a negative number. A value of 0.000 indicates NPV was less than 

0.0005 billion 2013$ and thus rounded to 0.000. 

† The NIA modeled the two typical sizes for IMH-W-Large-B, IMH-A-Large-B, and RCU-Large-B, 

rather than modeling these as one class. The values for these three classes are the totals of the B-1 

and B-2 typical units.

10B-14
Table 10B.1.13 Automatic Commercial Ice Makers: Net Present Value in Billions (2013$) 

at a 7-Percent Discount Rate – Constant Electric and Water Price Scenario

Equipment Class* Cumulative NPV by TSL at 7%

billions 2013$**

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH-W-Small-B 0.005 0.005 0.009 0.009 (0.053)

IMH-W- Med -B 0.009 0.009 0.009 0.004 (0.010)

IMH-W-Large-B† 0.000 0.000 0.000 0.000 (0.003)

 IMH-W-Large-B1 0.000 0.000 0.000 0.000 (0.002)

 IMH-W-Large-B2 0.000 0.000 0.000 0.000 (0.001)

IMH-A-Small-B 0.015 0.011 0.028 0.028 (0.249)

IMH-A-Large-B† 0.039 0.089 0.100 0.084 (0.007)

 IMH-A-Large-B1 0.039 0.090 0.099 0.084 (0.007)

 IMH-A-Large-B2 (0.001) (0.001) 0.000 0.000 0.000 

RCU-Large-B† 0.039 0.039 0.039 0.029 (0.001)

 RCU-Large-B1 0.037 0.037 0.037 0.027 0.001 

 RCU-Large-B2 0.002 0.002 0.002 0.002 (0.002)

SCU-W-Large-B 0.001 0.002 0.002 0.000 0.000 

SCU-A-Small-B 0.015 0.033 0.058 0.048 (0.081)

SCU-A-Large-B 0.013 0.047 0.050 (0.010) (0.010)

IMH-A-Small-C 0.005 0.008 0.013 0.013 (0.016)

IMH-A-Large-C 0.005 0.005 0.009 0.009 (0.003)

RCU-Small-C 0.002 0.004 0.007 0.007 (0.004)

SCU-A-Small-C 0.017 0.025 0.033 0.033 (0.067)

Total 0.165 0.277 0.356 0.253 (0.504)

*IMH: ice-making head; RCU: remote condenser unit; SCU: self-contained unit; A: Air cooled; 

W: Water cooled; B: Batch; C: Continuous 

** A value in parentheses is a negative number. A value of 0.000 indicates NPV was less than 

0.0005 billion 2013$ and thus rounded to 0.000. 

† The NIA modeled the two typical sizes for IMH-W-Large-B, IMH-A-Large-B, and RCU-Large-B, 

rather than modeling these as one class. The values for these three classes are the totals of the B-1 

and B-2 typical units.

10B-15
Table 10B.1.14 Automatic Commercial Ice Makers: Net Present Value in Billions (2013$) 

at a 3-Percent Discount Rate – Constant Electric and Water Price Scenario

Equipment Class* Cumulative NPV by TSL at 3%

billions 2013$**

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH-W-Small-B 0.011 0.011 0.019 0.019 (0.083)

IMH-W- Med -B 0.020 0.020 0.020 0.012 (0.014)

IMH-W-Large-B† 0.000 0.000 0.000 0.000 (0.004)

 IMH-W-Large-B1 0.000 0.000 0.000 0.000 (0.004)

 IMH-W-Large-B2 0.000 0.000 0.000 0.000 (0.001)

IMH-A-Small-B 0.033 0.034 0.072 0.072 (0.386)

IMH-A-Large-B† 0.081 0.187 0.209 0.212 0.055 

 IMH-A-Large-B1 0.081 0.187 0.207 0.209 0.052 

 IMH-A-Large-B2 (0.000) (0.000) 0.003 0.003 0.003 

RCU-Large-B† 0.080 0.080 0.080 0.069 0.019 

 RCU-Large-B1 0.076 0.076 0.076 0.065 0.021 

 RCU-Large-B2 0.004 0.004 0.004 0.004 (0.002)

SCU-W-Large-B 0.003 0.004 0.004 0.001 0.001 

SCU-A-Small-B 0.031 0.070 0.125 0.110 (0.126)

SCU-A-Large-B 0.027 0.098 0.106 (0.003) (0.003)

IMH-A-Small-C 0.011 0.017 0.027 0.027 (0.027)

IMH-A-Large-C 0.010 0.010 0.018 0.018 (0.002)

RCU-Small-C 0.005 0.008 0.015 0.015 (0.005)

SCU-A-Small-C 0.035 0.052 0.068 0.068 (0.115)

Total 0.346 0.589 0.763 0.620 (0.690)

*IMH: ice-making head; RCU: remote condenser unit; SCU: self-contained unit; A: Air cooled; 

W: Water cooled; B: Batch; C: Continuous 

** A value in parentheses is a negative number. A value of 0.000 indicates NPV was less than 

0.0005 billion 2013$ and thus rounded to 0.000. 

† The NIA modeled the two typical sizes for IMH-W-Large-B, IMH-A-Large-B, and RCU-Large-B, 

rather than modeling these as one class. The values for these three classes are the totals of the B-1 

and B-2 typical units.

10B.1.3 Nine-Year Sensitivity Analysis 

For this rulemaking, DOE undertook a sensitivity analysis using 9 years rather than 30 

years of product shipments. The choice of a 9-year period is a proxy for the timeline in Energy 

Policy and Conservation Act of 1975 (EPCA) for the review of certain energy conservation 

standards and potential revision of and compliance with such revised standards.a This timeframe 

may not be statistically relevant with regard to the product lifetime, product manufacturing 

cycles, or other factors specific to automatic commercial ice makers. Thus, this information is 

a For automatic commercial ice makers, DOE is required to review standards at least every 5 years after the effective 

date of any amended standards. (42 U.S.C. 6313(d)(3)(B)) If new standards are promulgated, EPCA requires DOE 

to provide manufacturers a minimum of 3 years and a maximum of 5 years to comply with the standards. (42 U.S.C. 

6313(d)(3)(C)) In addition, for certain other types of commercial equipment that are not specified in 42 U.S.C. 

6311(1)(B)-(G), EPCA requires DOE to review its standards at least once every 6 years (42 U.S.C. 6295(m)(1) and 

6316(a)), and either a 3-year or a 5-year period after any new standard is promulgated before compliance is required. 

(42 U.S.C. 6295(m)(4) and 6316(a)) As a result, DOE’s standards for automatic commercial ice makers can be 

expected to be in effect for 8 to 10 years between compliance dates, and its standards governing certain other 

commercial equipment, the period is 9 to 11 years. A 9-year analysis was selected as representative of the time 

between standard revisions.

10B-16

 
presented for informational purposes only and is not indicative of any change in DOE’s 

analytical methodology. The NPV results based on a 9-year analysis period are presented in 

Table 10B.1.15 and Table 10B.1.16 at 7-percent and 3-percent discount rates, respectively. The 

impacts are counted over the lifetime of equipment purchased in 2018–2026.

Table 10B.1.15 Automatic Commercial Ice Makers: Net Present Value at a 7-percent 

Discount Rate for 9-Year Analysis Period for Equipment Purchased in 2018–2026

Equipment Class* Cumulative NPV by TSL at 7%

billions 2013$**

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH-W-Small-B 0.003 0.003 0.005 0.005 (0.030)

IMH-W- Med -B 0.005 0.005 0.005 0.003 (0.004)

IMH-W-Large-B† 0.000 0.000 0.000 0.000 (0.001)

 IMH-W-Large-B1 0.000 0.000 0.000 0.000 (0.001)

 IMH-W-Large-B2 0.000 0.000 0.000 0.000 (0.000)

IMH-A-Small-B 0.009 0.009 0.018 0.018 (0.137)

IMH-A-Large-B† 0.021 0.051 0.057 0.036 (0.005)

 IMH-A-Large-B1 0.021 0.052 0.057 0.036 (0.006)

 IMH-A-Large-B2 (0.000) (0.000) 0.001 0.001 0.001 

RCU-Large-B† 0.021 0.021 0.021 0.018 0.004 

 RCU-Large-B1 0.020 0.020 0.020 0.017 0.005 

 RCU-Large-B2 0.001 0.001 0.001 0.001 (0.001)

SCU-W-Large-B 0.001 0.001 0.001 0.000 0.000 

SCU-A-Small-B 0.008 0.018 0.036 0.032 (0.030)

SCU-A-Large-B 0.007 0.028 0.030 0.001 0.001 

IMH-A-Small-C 0.003 0.004 0.007 0.007 (0.007)

IMH-A-Large-C 0.003 0.003 0.005 0.005 (0.000)

RCU-Small-C 0.001 0.002 0.004 0.004 (0.001)

SCU-A-Small-C 0.009 0.013 0.018 0.018 (0.030)

Total 0.090 0.158 0.207 0.147 (0.241)

* IMH: ice-making head; RCU: remote condenser unit; SCU: self-contained unit; A: Air cooled; 

W: Water cooled; B: Batch; C: Continuous 

** A value in parentheses is a negative number. A value of 0.000 indicates NPV was less than 

0.0005 billion 2013$ and thus rounded to 0.000.

† The NIA modeled the two typical sizes for IMH-W-Large-B, IMH-A-Large-B, and RCU-Large-B, 

rather than modeling these as one class. The values for these three classes are the totals of the B-1 

and B-2 typical units.

10B-17
Table 10B.1.16 Automatic Commercial Ice Makers: Net Present Value at a 3-percent 

Discount Rate for 9-Year Analysis Period for Equipment Purchased in 2018–2026

Equipment Class* Cumulative NPV by TSL at 3%

billions 2013$**

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH-W-Small-B 0.005 0.005 0.009 0.009 (0.038)

IMH-W- Med -B 0.008 0.008 0.008 0.006 (0.002)

IMH-W-Large-B† 0.000 0.000 0.000 0.000 (0.001)

 IMH-W-Large-B1 0.000 0.000 0.000 0.000 (0.001)

 IMH-W-Large-B2 0.000 0.000 0.000 0.000 (0.000)

IMH-A-Small-B 0.014 0.017 0.035 0.035 (0.168)

IMH-A-Large-B† 0.033 0.081 0.090 0.067 0.016 

 IMH-A-Large-B1 0.033 0.081 0.089 0.065 0.014 

 IMH-A-Large-B2 0.001 0.001 0.002 0.002 0.002 

RCU-Large-B† 0.032 0.032 0.032 0.031 0.015 

 RCU-Large-B1 0.030 0.030 0.030 0.030 0.016 

 RCU-Large-B2 0.002 0.002 0.002 0.002 (0.000)

SCU-W-Large-B 0.001 0.002 0.002 0.001 0.001 

SCU-A-Small-B 0.013 0.029 0.057 0.054 (0.029)

SCU-A-Large-B 0.011 0.043 0.047 0.010 0.010 

IMH-A-Small-C 0.004 0.007 0.011 0.011 (0.008)

IMH-A-Large-C 0.004 0.004 0.007 0.007 0.001 

RCU-Small-C 0.002 0.003 0.006 0.006 (0.001)

SCU-A-Small-C 0.014 0.021 0.028 0.028 (0.037)

Total 0.142 0.253 0.332 0.264 (0.241)

* IMH: ice-making head; RCU: remote condenser unit; SCU: self-contained unit; A: Air cooled; 

W: Water cooled; B: Batch; C: Continuous 

** A value in parentheses is a negative number. A value of 0.000 indicates NPV was less than 

0.0005 billion 2013$ and thus rounded to 0.000.

† The NIA modeled the two typical sizes for IMH-W-Large-B, IMH-A-Large-B, and RCU-Large-B, 

rather than modeling these as one class. The values for these three classes are the totals of the B-1 

and B-2 typical units.

10B.1.4 Shipment Price Sensitivity

For the reference case in this rulemaking, DOE did not include the impact on shipments 

due to price elasticity of demand. As reported in final rule TSD chapter 10, as well as in the 

notice of proposed rulemaking and preliminary analysis TSDs, during the preliminary analysis 

DOE asked manufacturers questions about price sensitivity as part of the preliminary

manufacturer impact analysis (MIA). The average of the responses indicated that a 10-percent 

increase in price would lead to a 6.25-percent decrease in shipments. DOE conducted extensive 

secondary research and was unable to find estimates of price elasticity. DOE also lacks sufficient 

data with which to develop an estimate. Thus, the results from the preliminary MIA provide the 

best information about possible price impacts.

When the analysis takes into account both the baseline distribution of equipment 

purchases by efficiency level and the incremental cost incurred on average to raise all customers’ 

efficiency level to TSL 3, if the analysis then weights the resulting cost increase using 2018

10B-18
shipments by equipment class,b the average TSL 3 increase is 2.7 percent. Thus, the estimated 

price impact is small. The NPV results for customer savings with a 7-percent discount rate, by 

TSL and equipment class, are shown on Table 10B.1.17. (Note that the maximum NPV 

efficiency levels [i.e., TSL 3] in the price sensitivity case match those of the reference case for 

all equipment classes, as does the efficiency level mapping for all other TSLs.) NPV results with 

a 3-percent discount rate are shown on Table 10B.1.18. Table 10B.1.19 shows cumulative energy 

savings by TSL.

Table 10B.1.17 Automatic Commercial Ice Makers: Net Present Value in Billions (2013$) 

at a 7-Percent Discount Rate – Equipment Price Elasticity Scenario

Equipment Class* Cumulative NPV by TSL at 7%

billions 2013$**

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH-W-Small-B 0.006 0.006 0.011 0.011 (0.048)

IMH-W- Med -B 0.010 0.010 0.010 0.005 (0.008)

IMH-W-Large-B† 0.000 0.000 0.000 0.000 (0.002)

 IMH-W-Large-B1 0.000 0.000 0.000 0.000 (0.002)

 IMH-W-Large-B2 0.000 0.000 0.000 0.000 (0.000)

IMH-A-Small-B 0.017 0.016 0.035 0.035 (0.233)

IMH-A-Large-B† 0.041 0.105 0.116 0.104 0.019 

 IMH-A-Large-B1 0.042 0.105 0.115 0.103 0.018 

 IMH-A-Large-B2 (0.000) (0.000) 0.001 0.001 0.001 

RCU-Large-B† 0.041 0.041 0.041 0.034 0.006 

 RCU-Large-B1 0.039 0.039 0.039 0.032 0.008 

 RCU-Large-B2 0.002 0.002 0.002 0.002 (0.001)

SCU-W-Large-B 0.002 0.002 0.002 0.001 0.001 

SCU-A-Small-B 0.016 0.035 0.074 0.066 (0.060)

SCU-A-Large-B 0.014 0.057 0.061 0.003 0.003 

IMH-A-Small-C 0.005 0.009 0.014 0.014 (0.014)

IMH-A-Large-C 0.005 0.005 0.009 0.009 (0.001)

RCU-Small-C 0.002 0.004 0.008 0.008 (0.003)

SCU-A-Small-C 0.018 0.026 0.035 0.035 (0.061)

Total 0.177 0.317 0.415 0.324 (0.401)

* IMH: ice-making head; RCU: remote condenser unit; SCU: self-contained unit; A: Air cooled; 

W: Water cooled; B: Batch; C: Continuous 

** A value in parentheses is a negative number. A value of 0.000 indicates NPV was less than 

0.0005 billion 2013$ and thus rounded to 0.000.

† The NIA modeled the two typical sizes for IMH-W-Large-B, IMH-A-Large-B, and RCU-Large-B, 

rather than modeling these as one class. The values for these three classes are the totals of the B-1 

and B-2 typical units.

b The distribution of shipments by equipment class is held constant throughout the analysis period. 

10B-19

 
Table 10B.1.18 Automatic Commercial Ice Makers: Net Present Value in Billions (2013$) 

at a 3-Percent Discount Rate – Equipment Price Elasticity Scenario

Equipment Class* Cumulative NPV by TSL at 3%

billions 2013$**

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH-W-Small-B 0.013 0.013 0.024 0.024 (0.073)

IMH-W- Med -B 0.021 0.021 0.021 0.015 (0.008)

IMH-W-Large-B† 0.000 0.000 0.000 0.000 (0.003)

 IMH-W-Large-B1 0.000 0.000 0.000 0.000 (0.003)

 IMH-W-Large-B2 0.000 0.000 0.000 0.000 (0.000)

IMH-A-Small-B 0.037 0.044 0.088 0.088 (0.353)

IMH-A-Large-B† 0.088 0.225 0.249 0.258 0.114 

 IMH-A-Large-B1 0.087 0.224 0.245 0.254 0.109 

 IMH-A-Large-B2 0.001 0.001 0.004 0.004 0.004 

RCU-Large-B† 0.085 0.085 0.085 0.081 0.036 

 RCU-Large-B1 0.081 0.081 0.081 0.076 0.037 

 RCU-Large-B2 0.004 0.004 0.004 0.004 (0.001)

SCU-W-Large-B 0.003 0.004 0.005 0.002 0.002 

SCU-A-Small-B 0.034 0.076 0.162 0.152 (0.075)

SCU-A-Large-B 0.029 0.122 0.133 0.029 0.029 

IMH-A-Small-C 0.011 0.018 0.029 0.029 (0.022)

IMH-A-Large-C 0.011 0.011 0.019 0.019 0.001 

RCU-Small-C 0.005 0.009 0.016 0.016 (0.002)

SCU-A-Small-C 0.037 0.055 0.073 0.073 (0.100)

Total 0.374 0.684 0.904 0.785 (0.455)

* IMH: ice-making head; RCU: remote condenser unit; SCU: self-contained unit; A: Air cooled; 

W: Water cooled; B: Batch; C: Continuous 

** A value in parentheses is a negative number. A value of 0.000 indicates NPV was less than 

0.0005 billion 2013$ and thus rounded to 0.000.

† The NIA modeled the two typical sizes for IMH-W-Large-B, IMH-A-Large-B, and RCU-Large-B, 

rather than modeling these as one class. The values for these three classes are the totals of the B-1 

and B-2 typical units.

10B-20
Table 10B.1.19 Automatic Commercial Ice Makers: Cumulative Energy Savings –

Equipment Price Elasticity Scenario

Equipment Class* National Energy Savings by EL

FFC energy quads**

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH-W-Small-B 0.002 0.002 0.004 0.004 0.009 

IMH-W- Med -B 0.005 0.005 0.005 0.008 0.010 

IMH-W-Large-B† 0.000 0.000 0.000 0.000 0.002 

 IMH-W-Large-B1 0.000 0.000 0.000 0.000 0.001 

 IMH-W-Large-B2 0.000 0.000 0.000 0.000 0.001 

IMH-A-Small-B 0.011 0.023 0.037 0.037 0.072 

IMH-A-Large-B† 0.019 0.034 0.039 0.059 0.075 

 IMH-A-Large-B1 0.017 0.031 0.035 0.055 0.072 

 IMH-A-Large-B2 0.002 0.002 0.003 0.003 0.003 

RCU-Large-B† 0.016 0.016 0.016 0.029 0.037 

 RCU-Large-B1 0.014 0.014 0.014 0.028 0.035 

 RCU-Large-B2 0.001 0.001 0.001 0.001 0.002 

SCU-W-Large-B 0.000 0.001 0.001 0.001 0.001 

SCU-A-Small-B 0.008 0.018 0.025 0.032 0.036 

SCU-A-Large-B 0.006 0.014 0.019 0.023 0.023 

IMH-A-Small-C 0.002 0.004 0.006 0.006 0.009 

IMH-A-Large-C 0.002 0.002 0.003 0.003 0.006 

RCU-Small-C 0.001 0.002 0.003 0.003 0.005 

SCU-A-Small-C 0.006 0.010 0.015 0.015 0.023 

Total 0.078 0.130 0.172 0.220 0.308 

* IMH: ice-making head; RCU: remote condenser unit; SCU: self-contained unit; A: Air cooled; 

W: Water cooled; B: Batch; C: Continuous 

** A value of 0.000 indicates energy savings rounded to less than 0.0005 quads and thus rounded to 

0.000.

† The NIA modeled the two typical sizes for IMH-W-Large-B, IMH-A-Large-B, and RCU-Large-B, 

rather than modeling these as one class. The values for these three classes are the totals of the B-1 

and B-2 typical units.

10B-21
APPENDIX 10C. TRIAL STANDARD LEVELS AND STANDARDS EQUATIONS

TABLE OF CONTENTS

10C.1 INTRODUCTION .......................................................................................................10C-1

10C.2 TRIAL STANDARD LEVEL SELECTION CRITERIA ...........................................10C-1

10C.3 TRIAL STANDARD LEVEL EQUATIONS .............................................................10C-6

10C.3.1 General Methodology ................................................................................10C-6

10C.3.2 Exceptions..................................................................................................10C-7

10C.3.3 Summary of Results...................................................................................10C-7

LIST OF TABLES

Table 10C.2.1 Mapping Between TSLs and Efficiency Levels*.............................................10C-2

Table 10C.2.2 Percentage Efficiency Improvement from Baseline by TSL* .........................10C-3

Table 10C.2.3 Design Options for Analyzed Products Classes at Each TSL..........................10C-4

Table 10C.2.4 Directly Analyzed Equipment Classes Used to Develop Standards for 

Secondary Classes............................................................................................10C-5

Table 10C.3.1 Energy Consumption by TSL for Representative Units Analyzed in the 

Engineering Analysis for Each Primary Equipment Class..............................10C-8

Table 10C.3.2 Equations Representing the TSLs for Batch Equipment Classes (Maximum 

Energy Use in kWh/100 lb ice)........................................................................10C-9

Table 10C.3.3 Equations Representing the TSLs for Continuous Equipment Classes 

(Maximum Energy Use in kWh/100 lb ice)...................................................10C-10

Table 10C.3.4 Equations Representing the Standards for Condenser Water ........................10C-11

10C-i
APPENDIX 10C. TRIAL STANDARD LEVELS AND STANDARDS EQUATIONS

10C.1 INTRODUCTION

The U.S. Department of Energy (DOE) carried out the life-cycle cost (LCC) analysis and 

national impact analysis (NIA) by defining efficiency levels within each equipment class of 

automatic commercial ice makers. These efficiency levels generally define pre-set efficiency 

improvements over base efficiencies and are generally consistent across equipment classes. For 

all equipment classes, the highest efficiency level listed corresponds to the maximum savings 

level. The actual value of this efficiency level differs widely. In addition, the number of 

efficiency levels specified for each equipment class varies widely, depending on the efficiency of 

the base equipment and the number of available technologies that can be analyzed for improving 

efficiency.

For three equipment classes, DOE analyzed 22-inch and non-22-inch variants. These 3 

equipment classes have “intermediate” efficiency levels—3A and 4A—at the percent efficiency 

improvements above the baseline where the 22-inch variants reach their maximum technology 

efficiency levels. The 3A and 4A efficiency levels apply only to these three classes.

DOE develops trial standard levels (TSLs) by combining selected efficiency levels from 

each equipment class into TSL groupings based on a set of specific criteria. This process enables 

the grouping of efficiency levels across all equipment classes such that the efficiency levels 

belonging to a TSL grouping all have a certain uniform characteristic or criterion associated with 

them. DOE evaluates the national impacts (e.g., national energy savings (NES), net present value 

(NPV), water savings, employment impacts, impacts on manufacturers, and environmental 

impacts) at each TSL. Based on the results, DOE proposes to set the standard at a particular TSL.

This appendix describes DOE’s method for selecting TSLs for automatic commercial ice 

makers. The following sections describe the criteria used for TSL selection and standard level 

equations associated with each TSL.

10C.2 TRIAL STANDARD LEVEL SELECTION CRITERIA

DOE selected five TSLs for this rulemaking based on the following criteria:

1) TSL 5 was set at the maximum technologically feasible (max-tech) level for each 

equipment class.

2) TSL 4 was chosen as an intermediate step between TSL 5 (the max-tech level) and 

TSL 3 (the maximum customer NPV level), subject to the requirement that the TSL 4 

NPV must be positive. Where a sufficient number of efficiency levels allow it, TSL 4 

is set at least one level below TSL 5 and one level above TSL 3. In one case, the TSL 

4 efficiency level equals the TSL 3 level because the next level had a negative NPV. 

In cases where the maximum NPV efficiency level is the penultimate efficiency level 

and the max-tech level showed a positive NPV, the TSL 4 efficiency level is also the 

max-tech level.

10C-1
3) TSL 3 was chosen to represent the efficiency level within each equipment class with 

the highest NPV at a 7-percent discount rate.

4) TSL 2 was selected to provide intermediate efficiency levels that fill the gap between 

the TSLs 1 and 3. Note that with the number of efficiency levels available for each 

equipment class, there is often overlap between TSL levels. Thus, TSL 2 includes 

levels that overlap with both TSLs 1 and 3. TSL 2 provides an intermediate level that 

compromises between TSLs 1 and 3.

5) TSL 1 was set equal to a 10-percent efficiency improvement over the base

Table 10C.2.1 presents the efficiency levels within each equipment class that belong to 

the five TSL groupings. Table 10C.2.2 presents the percentage efficiency improvements for each 

primary equipment class relative to the baseline energy-usage levels. 

Table 10C.2.1 Mapping Between TSLs and Efficiency Levels*

Equipment Class Baseline

Intermediate 

Level Max NPV Fill In MaxTech

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH-W-Small-B Level 1 Level 2 Level 2 Level 3 Level 3 Level 5

IMH-W-Med-B Level 1 Level 2 Level 2 Level 2 Level 3 Level 4

IMH-W-Large-B†

 IMH-W-Large-B1 Level 1 Level 1 Level 1 Level 1 Level 1 Level 2

 IMH-W-Large-B2 Level 1 Level 1 Level 1 Level 1 Level 1 Level 2

IMH-A-Small-B Level 1 Level 2 Level 3 Level 3A Level 3A Level 6

IMH-A-Large-B†

 IMH-A-Large-B1 Level 1 Level 2 Level 3 Level 3A Level 4 Level 5

 IMH-A-Large-B2 Level 1 Level 2 Level 2 Level 3 Level 3 Level 3

RCU-Large-B†

 RCU-Large-B1 Level 1 Level 2 Level 2 Level 2 Level 3 Level 4

 RCU-Large-B2 Level 1 Level 2 Level 2 Level 2 Level 2 Level 3

SCU-W-Large-B Level 1 Level 2 Level 4 Level 5 Level 6 Level 6

SCU-A-Small-B Level 1 Level 2 Level 4 Level 5 Level 6 Level 7

SCU-A-Large-B Level 1 Level 2 Level 4 Level 5 Level 6 Level 6

IMH-A-Small-C Level 1 Level 2 Level 3 Level 4 Level 4 Level 6

IMH-A-Large-C Level 1 Level 2 Level 2 Level 3 Level 3 Level 5

RCU-Small-C Level 1 Level 2 Level 3 Level 4 Level 4 Level 6

SCU-A-Small-C Level 1 Level 2 Level 3 Level 4 Level 4 Level 6

“Level” stands for “efficiency level.”

*For three large equipment classes—IMH-W-Large-B, IMH-A-Large-B and RCU-Large-B—because the harvest 

capacity range is so wide, DOE analyzed two typical models to model the low and the high portions of the applicable 

range with greater accuracy.. The smaller of the two is noted as B1 and the larger as B2.

†DOE analyzed impacts for the B1 and B2 typical units and aggregated impacts to the equipment class level.

10C-2
Table 10C.2.2 Percentage Efficiency Improvement from Baseline by TSL*

Equipment Class TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH-W-Small-B 10.0% 10.0% 15.0% 15.0% 23.9%

IMH-W-Med-B 10.0% 10.0% 10.0% 15.0% 18.1%

IMH-W-Large-B 0.0% 0.0% 0.0% 0.0% 8.1%

 IMH-W-Large-B1 0.0% 0.0% 0.0% 0.0% 8.3%

 IMH-W-Large-B2 0.0% 0.0% 0.0% 0.0% 7.4%

IMH-A-Small-B 10.0% 15.0% 18.1% 18.1% 25.5%

IMH-A-Large-B 10.0% 14.2% 15.2% 18.7% 21.6%

 IMH-A-Large-B1 10.0% 15.0% 15.8% 20.0% 23.4%

 IMH-A-Large-B2 10.0% 10.0% 11.8% 11.8% 11.8%

RCU-Large-B 10.0% 10.0% 10.0% 14.7% 17.1%

 RCU-Large-B1 10.0% 10.0% 10.0% 15.0% 17.3%

 RCU-Large-B2 10.0% 10.0% 10.0% 10.0% 13.9%

SCU-W-Large-B 10.0% 20.0% 25.0% 29.8% 29.8%

SCU-A-Small-B 10.0% 20.0% 25.0% 30.0% 32.7%

SCU-A-Large-B 10.0% 20.0% 25.0% 29.1% 29.1%

IMH-A-Small-C 10.0% 15.0% 20.0% 20.0% 25.7%

IMH-A-Large-C 10.0% 10.0% 15.0% 15.0% 23.3%

RCU-Small-C 10.0% 15.0% 20.0% 20.0% 26.6%

SCU-A-Small-C 10.0% 15.0% 20.0% 20.0% 26.6%

*Percentage improvements for IMH-W-Large-B, IMH-A-Large-B and RCU-Large-B are a 

weighted average of the B1 and B2 units.

Table 10C.2.3 shows the designs options that were used during the engineering analysis 

to achieve the percentage efficiency improvement of each TSL within each equipment class.

10C-3
Table 10C.2.3 Design Options for Analyzed Products Classes at Each TSL

Equipment Class Baseline TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

Design Options for Each TSL (options are cumulative – TSL 5 includes all preceding options)

IMH-W-Small-B No BW Fill 

SPM PM

+ Comp EER 

+ Cond

Same EL as 

TSL 1 + Cond Same EL as 

TSL 3

BW Fill 

+ Evap 

ECM PM 

DWHX

IMH-W-Small-B

(22 inch wide) No BW Fill 

SPM PM

+ Comp EER 

+ Cond

Same EL as 

TSL 1

+ Cond 

BW Fill

Same EL as 

TSL 3

N/A for 22-

inch

IMH-W-Med-B BW Fill 

SPM PM

+ Comp EER 

ECM PM

Same EL as 

TSL 1

Same EL as 

TSL 1 + Cond DWHX

IMH-W-Large-B1 BW Fill 

SPM PM

Same EL as 

Baseline

Same EL as 

Baseline

Same EL as 

Baseline

Same EL as 

Baseline

+ Comp EER 

+ Cond 

ECM PM

DWHX

IMH-W-Large-B2 BW Fill 

SPM PM

Same EL as 

Baseline

Same EL as 

Baseline

Same EL as 

Baseline

Same EL as 

Baseline

+ Comp EER 

+ Cond 

ECM PM

DWHX

IMH-A-Small-B

BW Fill 

SPM PM 

SPM FM

+ Comp EER 

+ Cond 

+ Evap 

ECM FM

+ Evap + Evap Same EL as 

TSL 3

+ Evap 

ECM PM 

DWHX

IMH-A-Small-B

(22 inch wide)

BW Fill 

SPM PM 

SPM FM

+ Comp EER 

+ Cond 

+ Evap 

ECM FM

+ Evap ECM PM 

DWHX 

Same EL as 

TSL 3

N/A for 22-

inch

IMH-A-Large-B1

No BW Fill

SPM PM

SPM FM

+ Comp EER

PSC FM

ECM FM 

BW Fill BW Fill

BW Fill 

ECM PM 

+ Cond

DWHX

IMH-A-Large-B1

(22 inch wide)

No BW Fill 

SPM PM 

SPM FM

+ Comp EER 

ECM FM 

BW Fill

BW Fill 

ECM PM 

DWHX

DWHX N/A for 22-

inch

N/A for 22-

inch

IMH-A-Large-B2

BW Fill 

SPM PM 

SPM FM

+ Comp EER 

ECM FM 

ECM PM 

+ Cond 

DWHX

Same EL as 

TSL 1

DWHX Same EL as 

TSL 3

Same EL as 

TSL 3

RCU-Large-B1

BW Fill 

SPM PM 

PSC FM

+ Cond 

+ Comp EER

Same EL as 

TSL 1

Same EL as 

TSL 1

ECM FM 

ECM PM 

+ Cond 

DWHX

DWHX

RCU-Large-B2

BW Fill 

SPM PM 

PSC FM

+ Comp EER 

ECM FM 

+ Cond 

ECM PM

Same EL as 

TSL 1

Same EL as 

TSL 1

Same EL as 

TSL 1 DWHX

EL = Efficiency Level

SPM = Shaded Pole Motor

PSC = Permanent Split Capacitor Motor

ECM = Electronically Commutated Motor

FM = Fan Motor (Air-Cooled Units)

AM = Auger Motor (Continuous Units)

BW Fill = Batch Water Fill Option Included 

+ Cond = Increase in Condenser Size

+ Evap = Increase in Evaporator Size

+ Comp EER = Increase in Compressor EER

DWHX = Addition of Drain Water Heat Exchanger

10C-4
Table 10C.2.3 (cont)

Equipment Class Baseline TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

Design Options for Each TSL (options are cumulative – TSL 5 includes all preceding options)

SCU-W-Large-B No BW Fill 

SPM PM

BW Fill 

+ Evap

+Evap 

+ Cond + Cond + Cond DWHX

SCU-A-Small-B

No BW Fill 

SPM PM 

SPM FM

+ Cond 

+ Comp EER + Comp EER PSC FM 

BW Fill

BW Fill 

ECM PM 

ECM FM

ECM FM 

DWHX

SCU-A-Large-B

No BW Fill 

SPM PM 

SPM FM

+Cond 

+ Comp EER

+ Comp EER 

BW Fill

BW Fill 

ECM FM

ECM PM 

DWHX

Same EL as 

TSL 4

RCU-Small-C PSC AM 

SPM FM

+ Comp EER 

PSC FM ECM FM ECM FM 

+ Cond

Same EL as

TSL3

+ Cond 

ECM AM

IMH-A-Small-C PSC AM 

SPM FM

+ Comp EER 

+ Cond

+ Cond 

ECM FM

ECM FM 

+ Cond

Same EL as 

TSL 3 ECM AM

IMH-A-Large-C PSC AM 

SPM FM + Comp EER Same EL as 

TSL 1

+ Comp EER 

+ Cond

Same EL as 

TSL 3

+ Cond 

ECM FM 

ECM AM

SCU-A-Small-C PSC AM 

SPM FM

+ Cond 

+ Comp EER + Comp EER + Comp EER ECM FM

Same EL as 

TSL 3

ECM FM 

ECM AM

EL = Efficiency Level

SPM = Shaded Pole Motor

PSC = Permanent Split Capacitor Motor

ECM = Electronically Commutated Motor

FM = Fan Motor (Air-Cooled Units)

AM = Auger Motor (Continuous Units)

BW Fill = Batch Water Fill Option Included 

+ Cond = Increase in Condenser Size

+ Evap = Increase in Evaporator Size

+ Comp EER = Increase in Compressor EER

DWHX = Addition of Drain Water Heat Exchanger

In the final rule analyses, DOE directly studied 13 equipment classes. The final rule 

proposes energy efficiency standards for the remaining equipment classes by extending results 

from primary equipment classes to secondary classes that are similar from an engineering 

perspective. Table 10C.2.3 maps the secondary equipment classes to those similar, directly 

analyzed equipment classes.

Table 10C.2.4 Directly Analyzed Equipment Classes Used to Develop Standards for 

Secondary Classes

Secondary 

Equipment Class

Directly Analyzed Product Class 

Associated with Efficiency Level 

for Secondary Product Class

RCU-NRC-Small-B RCU-NRC-Large-B

RCU-RC-Small-B RCU-NRC-Large-B

RCU-RC-Large-B RCU-NRC-Large-B

SCU-W-Small-B SCU-W-Large-B

IMH-W-Small-C IMH-A-Large-C

IMH-W-Large-C IMH-A-Large-C

RCU-NRC-Large-C RCU-NRC-Small-C

RCU-RC-Small-C RCU-NRC-Small-C

RCU-RC-Large-C RCU-NRC-Small-C

SCU-W-Small-C SCU-A-Small-C

SCU-W-Large-C SCU-A-Small-C

SCU-A-Large-C SCU-A-Small-C

10C-5
DOE briefly considered two additional criteria for TSL groupings. One criterion was to 

group the efficiency levels that exclude max-tech design options. However, because this criterion 

was not significantly different from the max NPV level, it was discarded. Another criterion

considered setting standards to a 20-percent across-the-board efficiency improvement. This 

criterion was discarded because not all equipment classes exhibit desirable customer results at 20 

percent. 

10C.3 TRIAL STANDARD LEVEL EQUATIONS

Current energy conservation standards for automatic commercial ice makers specify 

maximum energy consumption levels per 100 lb of ice per 24-hour period. (42 U.S.C. 

6313(d)(1)) Similarly, for automatic commercial ice makers using water for cooling in the 

condenser, water limits are established in terms of gallons of water per 100 lb of ice per 24-hour 

period. In this notice, DOE has retained these metrics for specifying energy and water 

conservation standards.

10C.3.1 General Methodology

Developing trial standard level equations required several steps. In developing TSLs, 

DOE analyzed each equipment class separately and attributed a percentage reduction with each 

portion of the equipment standard curve (small/medium/large). To ensure that the standard curve 

was continuous (i.e., no gaps at the breakpoints between size categories), DOE developed a 

method for expressing the consumption standards. The method relied on two techniques:

1) adjusting the high-capacity equipment classes (whose equations were constant with 

respect to capacity) by a factor equal to the expected percentage reduction

2) pivoting the low-capacity equipment classes (whose equations were a function of 

capacity) about a representative point while staying connected to the high-capacity 

equation.

These steps are described in further detail below. DOE was able to use the same 

methodology for most equipment classes, with exceptions for the IMH-W-Batch, IMH-A-Batch, 

and RCU-RC equipment classes.

In drawing a relationship between the harvest capacity (lb ice/24 hours) and the 

maximum allowed energy usage (kilowatt-hours per 100 lb of ice), DOE first took the largecapacity equipment class (which is set at a constant value for all equipment types except IMH-ALarge-B) and applied the allocated percentage reduction (i.e., the percentage reduction associated 

with the TSL for that equipment class). For example, for IMH-W-Large-B, the baseline level is 

set at 4.0. If the TSL allocated a 10-percent reduction for IMH-W-Large-B, then the next level 

was set at 4.0 × (1-10 percent) = 3.6 kWh/100 lb of ice.

Next, for the small equipment classes, DOE applied the allocated percentage reduction at 

a designated median capacity in that harvest rate range. The medium capacity was selected based 

on shipment levels and where the median fell within the shipments data. For example, if the 

median capacity for the small equipment class was at 300 lb ice/24 hours, DOE would calculate 

the baseline energy usage and then apply the allocated percentage reduction to obtain a point at 

10C-6
300 lb ice/24 hours. DOE would then draw a line between the start of the large equipment class 

and this median capacity point to obtain the equation for the small equipment class, ensuring that 

there were no gaps between small and large-capacity equipment class standards. This method 

generally produced trial standard level equations that were consistent with the expected 

percentage reduction for the small equipment classes.

10C.3.2 Exceptions

For the IMH-W-Batch equipment classes, this equipment type has small, medium, and 

large equipment classes. In this case, for the small equipment class, DOE applied the allocated 

percentage reduction to the whole equation. So if the percentage reduction was 10 percent, the 

new equation for the small equipment class would be (1-10 percent) × (7.80 – 0.0055H) = 7.02 –

0.00495H. The method for treating the large equipment class would remain the same. DOE 

would then draw a line between the end of the small equipment class and the start of the large 

equipment class to obtain the equation for the medium equipment class.

For the IMH-A-Batch equipment classes, DOE sought to obtain a constant efficiency 

level for the largest equipment classes. In order to achieve this, for the small equipment class, 

DOE applied the allocated percentage reduction to the whole equation. DOE then analyzed two 

different sizes of equipment in the IMH-A-Large-B equipment class. For the largest size of 

equipment, DOE chose a pivot point at 1500 lb/day and applied the allocated percentage 

reduction for this size; the trial standard level for the largest sizes was set at a constant efficiency 

level equal to this value. For the smaller size of the IMH-A-Large-B equipment, DOE simply 

drew a line between the IMH-A-Small-B trial standard level and the trial standard level for the 

largest IMH-A-Large-B equipment. Further details are available in section 5.10.3 of chapter 5 of 

this technical support document.

For the RCU-RC-B and RCU-RC-C equipment classes, DOE simply took the standard 

levels calculated for the large RCU-NRC-B and RCU-NRC-C equipment classes, respectively, 

and subtracted the 0.2 kWh/100 lb of ice differential discussed in chapter 5 (for suction in the 

lines connecting the ice-making portion of the machine to the remote condenser) to arrive at the 

standard levels. For small RCU classes, remote compressor standards were developed such that 

no gap exists at the harvest rate breakpoints.

10C.3.3 Summary of Results

Table 10C.3.1 provides an example of the resulting energy consumption for 

representative units analyzed as part of the final rule analyses. Table 10C.3.2 and Table 10C.3.3

show standard equations for batch and continuous classes, respectively, at each TSL. In all of the 

standards equations, harvest rate or H is harvest rate in pounds per 24 hours.

10C-7
Table 10C.3.1 Energy Consumption by TSL for Representative Units Analyzed in the 

Engineering Analysis for Each Primary Equipment Class

Equipment Class

Representative 

Harvest Rate

lb ice/24 hours

Energy Consumption of the 

Representative Automatic Commercial 

Ice Maker Unit

kWh/100 lb

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH-W-Small-B 300 5.54 5.54 5.23 5.23 4.67

IMH-W-Med-B 850 4.18 4.18 4.18 3.95 3.76

IMH-W-Large-B1 1500 4.00 4.00 4.00 4.00 3.68

IMH-W-Large-B2 2600 4.00 4.00 4.00 4.00 3.72

IMH-A-Small-B 300 6.91 6.53 6.30 6.30 5.68

IMH-A-Large-B1 800 5.41 5.11 5.05 4.81 4.63

IMH-A-Large-B2 1500 4.72 4.72 4.61 4.61 4.61

RCU-Large-B1 1500 4.59 4.59 4.59 4.34 4.23

RCU-Large-B2 2400 4.59 4.59 4.59 4.59 4.39

SCU-W-Large-B 300 6.84 6.08 5.70 5.32 5.17

SCU-A-Small-B 110 11.56 10.27 9.63 8.99 8.60

SCU-A-Large-B 200 8.82 7.84 7.35 6.96 6.96

IMH-A-Small-C 310 8.15 7.69 7.24 7.24 6.43

IMH-A-Large-C 820 5.94 5.94 5.61 5.61 5.08

RCU-Small-C 800 5.70 5.38 5.06 5.06 4.62

SCU-A-Small-C 220 9.11 8.60 8.10 8.10 7.29

10C-8
Table 10C.3.2 Equations Representing the TSLs for Batch Equipment Classes (Maximum Energy Use in kWh/100 lb ice)

Batch Equipment 

Class

Capacity Range 

lb/day TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH-W-Small-B <300 7.19 – 0.0055H 7.19 – 0.0055H 6.88 – 0.0055H 6.88 – 0.0055H 6.32 – 0.0055H

IMH-W-Med-B ≥300 and <850 6.28 – 0.00247H 6.28 – 0.00247H 5.8 – 0.00191H 5.9 – 0.00224H 5.17 – 0.00165H

IMH-W-Large-B ≥850 and <1500 4.42 – 0.00028H 4.42 – 0.00028H

4.0 4.0

3.86 – 0.00012H

IMH-W-Extended-B ≥1,500 and <2,600 4.0 4.0 3.62 + 0.00004H

≥2,600 3.72

IMH-A-Small-B <300 10.09 – 0.0106H 10.05 – 0.01173H 10 – 0.01233H 10 – 0.01233H 9.38 – 0.01233H

IMH-A-Medium-B ≥300 and <800 7.81 – 0.003H 7.38 – 0.00284H 7.05 – 0.0025H 7.19 – 0.00298H 6.31 – 0.0021H

IMH-A-Large-B ≥800 and <1,500 6.21 – 0.00099H 5.56 – 0.00056H 5.55 – 0.00063H 5.04 – 0.00029H 4.65 – 0.00003H

IMH-A-Extended-B >1,500 4.73 4.72 4.61 4.61 4.61

RCU-NRC-Small-B <1,000 7.97 – 0.00342H 7.97 – 0.00342H 7.97 – 0.00342H 7.52 – 0.00323H 7.35 – 0.00316H

RCU-NRC-Large-B ≥1,000 and <1,500

4.55 4.55 4.55 4.29

4.19

RCU-NRC-Extended-B ≥1,500 and < 2,400 3.93 + 0.00017H

≥2,400 4.34

RCU-RC-Small-B <942 7.97 – 0.00342H 7.97 – 0.00342H 7.97 – 0.00342H 7.52 – 0.00323H 7.35 – 0.00316H

RCU-RC-Large-B ≥942 and < 1,500

4.75 4.75 4.75 4.49

4.39

RCU-RC-Extended-B ≥1,500 and < 2,400 4.13 + 0.00017H

≥2,400 4.54

SCU-W-Small-B <200 10.64 – 0.019H 9.88 – 0.019H 9.5 – 0.019H 9.14 – 0.019H 9.14 – 0.019H

SCU-W-Large-B ≥200 6.84 6.08 5.7 5.34 5.34

SCU-A-Small-B <110 16.72 – 0.0469H 15.43 – 0.0469H 14.79 – 0.0469H 14.15 – 0.0469H 13.76 – 0.0469H

SCU-A-Large-B ≥110 and <200 14.91 –

0.03044H 13.24 – 0.027H 12.42 – 0.02533H 11.47 – 0.02256H 10.6 – 0.02

SCU-A-Extended-B ≥200 8.82 7.84 7.35 6.96 6.96

10C-9
Table 10C.3.3 Equations Representing the TSLs for Continuous Equipment Classes (Maximum Energy Use in kWh/100 lb ice)

Continuous Equipment 

Class

Capacity Range 

lb/day TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH-W-Small-C <801 7.29 – 0.003H 6.89 – 0.00283H 6.48 – 0.00267H 6.48 – 0.00267H 5.75 – 0.00237H

IMH-W-Large-C ≥801 4.59 4.59 4.34 4.34 3.93

IMH-A-Small-C <310 10.1 – 0.00629H 9.64 – 0.00629H 9.19 – 0.00629H 9.19 -0.00629H 8.38 – 0.00629H

IMH-A-Large-C ≥310 and <820 9.49 – 0.00433H 8.75 – 0.00343H 8.23 – 0.0032H 8.23 – 0.0032H 7.25 – 0.00265H

IMH-A-Extended-C ≥820 5.94 5.94 5.61 5.61 5.08

RCU-NRC-Small-C <800 9.85 – 0.00519H 9.78 – 0.0055H 9.7 – 0.0058H 9.7 – 0.0058H 9.26 – 0.0058H

RCU-NRC-Large-C ≥800 5.7 5.38 5.06 5.06 4.62

RCU-RC-Small-C <800 10.05 – 0.00519H 9.98 – 0.0055H 9.9 – 0.0058H 9.9 – 0.0058H 9.46 – 0.0058H

RCU-RC-Large-C ≥800 5.9 5.58 5.26 5.26 4.82

SCU-W-Small-C <900 8.55 – 0.0034H 8.08 0.0032H 7.6 – 0.00302H 7.6 – 0.00302H 6.84 – 0.00272H

SCU-W-Large-C ≥900 5.49 5.19 4.88 4.88 4.39

SCU-A-Small-C <200 15.26 – 0.03 14.73 – 0.03H 14.22 -0.03H 14.22 – 0.03H 13.4 – 0.03H

SCU-A-Large-C ≥200 and 700 10.66 – 0.00702H 10.06 – 0.00663H 9.47 – 0.00624H 9.47 – 0.00624H 8.52 – 0.00562H

SCU-A-Extended-C ≥700 5.75 5.42 5.1 5.1 4.59

10C-10
In the notice of proposed rulemaking (NOPR) analyses, DOE concluded that condenser 

water-changing design options were not cost-effective, and DOE did not examine these design 

options. Thus, DOE did not propose to revise the condenser water usage limits for equipment 

currently subject to efficiency standards. This conclusion has been maintained for the final rule. 

Existing condenser water standards will apply to batch type equipment not previously covered by 

standards. For equipment with harvest rates greater than or equal to 2,500 lb/24 hours and less 

than 4,000 lb/24 hours, DOE set a constant water limit for each equipment class, calculated using 

the existing equation for a machine with a harvest rate of 2,500 lb/24 hour. Based on equipment 

testing performed by DOE for this rulemaking, DOE set condenser water limits for continuous 

type machines at 80 percent of the limits set for batch type machines. 

Table 10C.3.4 lists the condenser water limits by equipment class. The proposed 

condenser water limits are the same for all five TSLs. The limits shown on Table 10C.3.4 are for 

condenser water only, not potable water.

Table 10C.3.4 Equations Representing the Standards for Condenser Water

Equipment Type Type of 

Cooling

Harvest Rate

lb ice / 24 hours

Maximum 

Condenser 

Water Use

gal/100 lb ice

Batch Type Equipment

Ice-Making Head Water

<300 200 – 0.022H

≥300 and <850 200 – 0.022H

≥850 and <1,500 200 – 0.022H

≥1,500 and <2,500 200 – 0.022H

≥2,500 and <4,000 145

Self-Contained Water

<200 191 – 0.0315H

≥200 and <2,500 191 – 0.0315H

≥2,500 and <4,000 112

Continuous Type Equipment

Ice-Making Head Water

<801 180 – 0.0198H

≥801 and <2,500 180 – 0.0198H

≥2,500 and <4,000 130.5

Self-Contained Water

<900 153 – 0.0252H

≥900 and <2,500 153 – 0.0252H

≥2,500 and <4,000 90

10C-11
APPENDIX 10D. FULL-FUEL-CYCLE MULTIPLIERS

TABLE OF CONTENTS

10D.1 INTRODUCTION ...................................................................................................... 10D-1

10D.2 METHODOLOGY ..................................................................................................... 10D-1

10D.3 FULL-FUEL-CYCLE ENERGY MULTIPLIERS .................................................... 10D-3

REFERENCES ....................................................................................................................... 10D-4

LIST OF TABLES

Table 10D.2.1 Dependence of FFC Parameters on AEO Inputs............................................. 10D-3

Table 10D.3.1 Full-Fuel-Cycle Energy Multipliers (Based on AEO2014) ............................ 10D-3

10D-i
APPENDIX 10D. FULL-FUEL-CYCLE MULTIPLIERS

10D.1 INTRODUCTION

This appendix summarizes the methods used to calculate full-fuel-cycle (FFC) energy 

savings expected to result from potential standards. The FFC measure includes point-of-use (site) 

energy; the energy losses associated with generation, transmission, and distribution of electricity;

and the energy consumed in extracting, processing, and transporting or distributing primary 

fuels. The U.S. Department of Energy’s (DOE’s) traditional approach encompassed only site 

energy and the energy losses associated with generation, transmission, and distribution of 

electricity. Per DOE’s 2011 Statement of Policy for Adopting Full Fuel Cycle Analyses, DOE 

now uses FFC measures of energy use and emissions in its energy conservation standards 

analyses. 76 FR 51281 (Aug. 18, 2011), as amended at 77 FR 49701 (Aug. 17, 2012). This 

appendix summarizes the methods used to incorporate the FFC impacts into the analysis. 

This analysis uses several different terms to reference energy use. The physical sources of 

energy are the primary fuels such as coal, natural gas, liquid fuels, etc. Primary energy is equal to 

the heat content (British thermal units) of the primary fuels used to provide an end-use service. 

Site energy use is defined as the energy consumed at the point-of-use in a building or industrial 

process. Where natural gas and petroleum fuels are consumed at the site (for example, in a 

furnace), site energy is identical to primary energy, with both equal to the heat content of the 

primary fuel consumed. For electricity, site energy is measured in kilowatt-hours. In this case,

the primary energy is equal to the quadrillion British thermal units (quads) of primary energy 

required to generate and deliver the site electricity. This primary energy is calculated by 

multiplying the site kilowatt-hours by the site-to-power plant energy use factor, given in 

chapter 10. For the FFC analysis, the upstream energy use is defined as the energy consumed in 

extracting, processing, and transporting or distributing primary fuels. FFC energy use is the sum 

of primary plus upstream energy use. 

Both primary fuels and electricity are used in upstream activities. The treatment of 

electricity in fuel cycle analysis must distinguish between electricity generated by fossil fuels 

and uranium, and electricity generated from renewable fluxes (wind, solar, and hydro). For the 

former, the upstream fuel cycle impacts are derived from the amount of fuel consumed at the 

power plant. For the latter, no fuel per se is used, so there is no upstream component.

10D.2 METHODOLOGY

The mathematical approach is discussed in the paper A Mathematical Analysis of Full 

Fuel Cycle Energy Use,

1 and details on the fuel production chain analysis are presented in the 

paper Projections of Full Fuel Cycle Energy and Emissions Metrics.

2 The following discussion

provides a brief summary of the methods used to calculate FFC energy. 

When all energy quantities are normalized to the same units, the FFC energy use can be 

represented as the product of the primary energy use and an FFC multiplier. The FFC multiplier 

is defined mathematically as a function of a set of parameters representing the energy intensity 

and material losses at each production stage. These parameters depend only on physical data, so 

10D-1
the calculations do not require any assumptions about prices or other economic data. While in 

general these parameter values may vary by geographic region, for this analysis national 

averages are used. 

In the notation below, the indices x and y are used to indicate fuel type, with x=c for coal, 

x=g for natural gas, x=p for petroleum fuels, x=u for uranium, and x=r for renewable fluxes. The 

fuel cycle parameters are the following: 

• ax is the quantity of fuel x burned per unit of electricity output, on average, for grid 

electricity. The calculation of ax includes a factor to account for transmission and 

distribution system losses. 

• by is the amount of grid electricity used in production of fuel y, in megawatt-hours 

per physical unit of fuel y. 

• cxy is the amount of fuel x consumed in producing one unit of fuel y. 

• qx is the heat content of fuel x (million British thermal units/physical unit) 

• zx(s) is the emissions intensity for fuel x (mass of pollutant s per physical unit of x).

The parameters are calculated as a function of time with an annual time step; hence, a 

time series of annual values is used to estimate the FFC energy and emissions savings in each 

year of the analysis period. Fossil fuel quantities are converted to energy units using the heat 

content factors qx. To convert electricity in kilowatt-hours to primary energy units, on-site 

electricity consumption is multiplied by the site-to-power plant energy use factor. The site-topower plant energy use factor is defined as the ratio of the total primary energy consumption by 

the electric power sector (in quads) divided by the total electricity generation in each year. 

The FFC multiplier is denoted μ (mu). A separate multiplier is calculated for each fuel 

used on site. A multiplier is also calculated for electricity reflecting the fuel mix used in its 

generation. The multipliers are dimensionless numbers that are applied to primary energy 

savings to obtain the FFC energy savings. The upstream component of the energy savings is 

proportional to (μ-1). The fuel type is denoted by a subscript on the multiplier μ. 

For DOE’s appliance standards energy savings estimates, the fuel cycle analysis 

methodology is designed to make use of data and projections published in the Annual Energy 

Outlook (AEO). Table 10D.2.1 provides a summary of the AEO data used as inputs to the 

different parameter calculations. The AEO does not provide all the information needed to 

estimate total energy use in the fuel production chain. Reference 2 describes the additional data 

sources used to complete the analysis. However, the time dependence in the FFC multipliers 

arises exclusively from variables taken from the AEO. The FFC analysis for automatic 

commercial ice-makers used data from Annual Energy Outlook 2014 (AEO2014).

3 AEO2014

provides projections to 2040.

10D-2
Table 10D.2.1 Dependence of FFC Parameters on AEO Inputs

Parameter Fuel AEO Table Variables

qx all Conversion Factors MMBtu per physical unit 

ax all

Electricity Supply, Disposition, Prices, and 

Emissions Generation by fuel type 

Energy Consumption by Sector and Source Electric power sector 

energy consumption 

bc, cnc, cpc coal Coal Production by Region and Type Production by coal type 

and sulfur content 

bp, cnp, cpp petroleum

Refining Industry Energy Consumption Refining only energy use 

Liquid Fuels Supply and Disposition Crude supply by source 

International Liquids Supply and Disposition Crude oil imports 

Oil and Gas Supply Crude oil domestic 

production 

cnn

natural 

gas

Oil and Gas Supply U.S. dry gas production 

Natural Gas Supply, Disposition and Prices Pipeline, lease and plant 

fuel 

zx all Electricity Supply, Disposition, Prices and Emissions Power sector emissions 

10D.3 FULL-FUEL-CYCLE ENERGY MULTIPLIERS 

FFC energy multipliers are presented in Table 10D.3.1 for selected years. To extend the 

analysis period beyond 2040, the last year in the AEO2014 projection, the multipliers are 

assumed constant through the final year of the analysis period. The multiplier for electricity 

reflects the shares of various primary fuels in total electricity generation over the forecast period. 

Table 10D.3.1 Full-Fuel-Cycle Energy Multipliers (Based on AEO2014)

2015 2020 2025 2030 2035 2040

Electricity (power plant primary 

energy use) 1.043 1.044 1.045 1.046 1.047 1.047

Natural Gas (site) 1.108 1.109 1.111 1.113 1.114 1.114

Petroleum Fuels (site) 1.176 1.176 1.176 1.174 1.172 1.170

10D-3
REFERENCES

1. Coughlin, K. A Mathematical Analysis of Full Fuel Cycle Energy Use. Energy, 2012. 

37(1): pp. 698-708. (Last accessed July 8, 2013.) 

<www.sciencedirect.com/science/article/pii/S0360544211006803>.

2. Coughlin, K. 2013. Projections of Full-Fuel-Cycle Energy and Emissions Metrics. 

Lawrence Berkeley National Laboratory, Berkeley, CA. Report No. LBNL-6025E.

3. U.S. Department of Energy - Energy Information Administration. Annual Energy Outlook 

2014. April 2014. Washington, D.C. <www.eia.gov/forecasts/aeo/pdf/0383%282014%29.pdf>.

10D-4
APPENDIX 10E. RISC & OIRA CONSOLIDATED INFORMATION SYSTEM 

(ROCIS) TABLES

TABLE OF CONTENTS

10E.1 INTRODUCTION .......................................................................................................10E-1

LIST OF TABLES

Table 10E.1.1 Cumulative and Annualized Benefits and Costs of Considered Standard Levels 

for ACIM Units Shipped in the Period 2018–2047 (TSL 1, 3 Percent Discount 

Rate).................................................................................................................10E-2

Table 10E.1.2 Cumulative and Annualized Benefits and Costs of Considered Standard Levels 

for ACIM Units Shipped in the Period 2018–2047 (TSL 2, 3 Percent Discount 

Rate).................................................................................................................10E-4

Table 10E.1.3 Cumulative and Annualized Benefits and Costs of Considered Standard Levels 

for ACIM Units Shipped in the Period 2018–2047 (TSL 3, 3 Percent Discount 

Rate).................................................................................................................10E-6

Table 10E.1.4 Cumulative and Annualized Benefits and Costs of Considered Standard Levels 

for ACIM Units Shipped in the Period 2018–2047 (TSL 4, 3 Percent Discount 

Rate).................................................................................................................10E-8

Table 10E.1.5 Cumulative and Annualized Benefits and Costs of Considered Standard Levels 

for ACIM Units Shipped in the Period 2018–2047 (TSL 5, 3 Percent Discount 

Rate)...............................................................................................................10E-10

Table 10E.1.6 Cumulative and Annualized Benefits and Costs of Considered Standard Levels 

for ACIM Units Shipped in the Period 2018–2047 (TSL 1, 7 Percent Discount 

Rate)...............................................................................................................10E-12

Table 10E.1.7 Cumulative and Annualized Benefits and Costs of Considered Standard Levels 

for ACIM Units Shipped in the Period 2018–2047 (TSL 2, 7 Percent Discount 

Rate)...............................................................................................................10E-14

Table 10E.1.8 Cumulative and Annualized Benefits and Costs of Considered Standard Levels 

for ACIM Units Shipped in the Period 2018–2047 (TSL 3, 7 Percent Discount 

Rate)...............................................................................................................10E-16

Table 10E.1.9 Cumulative and Annualized Benefits and Costs of Considered Standard Levels 

for ACIM Units Shipped in the Period 2018–2047 (TSL 4, 7 Percent Discount 

Rate)...............................................................................................................10E-18

Table 10E.1.10 Cumulative and Annualized Benefits and Costs of Considered Standard 

Levels for ACIM Units Shipped in the Period 2018–2047 (TSL 5, 7 Percent 

Discount Rate) ...............................................................................................10E-20

10E-i
APPENDIX 10E. RISC & OIRA CONSOLIDATED INFORMATION SYSTEM (ROCIS) 

TABLES

10E.1 INTRODUCTION

The net present value (NPV) of the monetized benefits associated with emissions 

reductions can be viewed as a complement to the NPV of the customer savings calculated for 

each trial standard level (TSL) considered in this final rule for automatic commercial ice-making

(ACIM) equipment. In Table 10E.1.1 through Table 10E.1.10, the top half of the table presents

the NPV values that would result if the U.S. Department of Energy (DOE) were to add the 

estimates of the potential economic benefits resulting from reduced carbon dioxide (CO2) and 

nitrogen oxide (NOx) emissions to the NPV of customer savings calculated for each TSL 

considered in this final rule, at both a 7-percent and 3-percent discount rate. 

Although combining the values of operating savings and CO2 reductions provides a 

useful perspective, two issues should be considered. First, the national operating savings are 

domestic U.S. consumer monetary savings that occur as a result of market transactions, while the 

value of CO2 reductions is based on a global value. Second, the assessments of operating cost 

savings and CO2 savings are performed with different methods that use quite different time 

frames for analysis. The national operating cost savings is measured for the lifetime of products 

shipped in the 30-year analysis period. The social cost of carbon values, on the other hand, 

reflect the present value of future climate-related impacts resulting from the emission of one ton 

of CO2 in each year. These impacts go well beyond 2100.

The benefits and costs of the considered standard levels, for products sold in 2018

through 2047, also can be expressed in terms of annualized values. The annualized monetary 

values shown in Table 10E.1.1 through Table 10E.1.10 present the sum of (1) the annualized 

national economic value, expressed in 2013 dollars (2013$), of the benefits from customer 

operation of products that meet the considered standard levels (consisting primarily of operating 

cost savings from using less energy, minus increases in equipment purchase and installation 

costs, which is another way of representing customer NPV) and (2) the annualized monetary 

value of the benefits of emission reductions, including CO2 emission reductions.

10E-1
Table 10E.1.1 Cumulative and Annualized Benefits and Costs of Considered Standard Levels for ACIM Units Shipped in the 

Period 2018–2047 (TSL 1, 3 Percent Discount Rate)

Units

IMHWSmallBIMHWMedBIMHWLargeBIMHASmallBRCU-

***-

LargeB

IMHALargeBSCUWLargeBSCUASmallBSCUALargeBIMHASmallCIMHALargeCRCU-

***-

SmallC

SCUASmallC

Total

Cumulative Results 

Energy Savings

Full-Fuel Cycle quads 0.002 0.005 0.000 0.011 0.016 0.020 0.000 0.008 0.006 0.002 0.002 0.001 0.007 0.081

Economic Impacts

Incremental Equipment Cost billion 2013$ 0.006 0.008 0.000 0.028 0.014 0.030 0.001 0.013 0.009 0.003 0.001 0.001 0.004 0.116

Operating Cost Savings billion 2013$ 0.020 0.030 0.000 0.067 0.102 0.121 0.004 0.048 0.039 0.014 0.012 0.006 0.042 0.505

NPV billion 2013$ 0.014 0.022 0.000 0.039 0.088 0.091 0.003 0.035 0.030 0.012 0.011 0.005 0.038 0.389

Emissions Savings (physical)

Full-Fuel Cycle

CO2

million metric 

ton 0.146 0.301 0.000 0.697 0.987 1.207 0.379 0.143 0.119 0.055 0.405 1.050 1.074 6.562

NOx kilo-ton 0.216 0.445 0.000 1.030 1.460 1.786 0.561 0.211 0.176 0.081 0.599 1.554 1.589 9.709

Hg Ton 0.000 0.001 0.000 0.002 0.003 0.003 0.001 0.000 0.000 0.000 0.001 0.003 0.003 0.017

SO2 kilo-ton 0.124 0.255 0.000 0.590 0.836 1.022 0.321 0.121 0.101 0.046 0.343 0.889 0.910 5.558

Emissions Savings 

(monetized)

Full-Fuel Cycle

CO2 (global)

5% dr, average million 2013$ 1.076 2.212 0.000 5.121 7.255 8.876 2.789 1.049 0.875 0.403 2.975 7.722 7.899 48.251

3% dr, average million 2013$ 4.820 9.913 0.000 22.951 32.510 39.774 12.498 4.701 3.920 1.805 13.331 34.606 35.399 216.229

2.5% dr, average million 2013$ 7.618 15.667 0.000 36.273 51.381 62.862 19.753 7.430 6.196 2.854 21.069 54.695 55.948 341.745

3% dr, 95th perc million 2013$ 14.878 30.599 0.000 70.843 100.350 122.772 38.578 14.510 12.100 5.573 41.148 106.820 109.268 667.440

NOx (3% dr)

At 476 2013$/ton million 2013$ 0.056 0.116 0.000 0.269 0.381 0.466 0.146 0.055 0.046 0.021 0.156 0.405 0.414 2.531

At 2,684 2013$/ton million 2013$ 0.318 0.655 0.000 1.515 2.147 2.626 0.825 0.310 0.259 0.119 0.880 2.285 2.337 14.278

At 4,893 2013$/ton million 2013$ 0.580 1.193 0.000 2.762 3.913 4.787 1.504 0.566 0.472 0.217 1.604 4.165 4.260 26.024

NPV

Consumer & Emissions Value 

Consumers + CO2 (1st) + NOx 

(Med) billion 2013$ 0.015 0.025 0.000 0.046 0.097 0.103 0.033 0.013 0.012 0.006 0.042 0.100 0.095 0.588

Consumers + CO2 (2nd) + NOx 

(Med) billion 2013$ 0.019 0.033 0.000 0.064 0.123 0.134 0.043 0.017 0.015 0.007 0.052 0.127 0.123 0.756

Consumers + CO2 (3rd) + NOx 

(Med) billion 2013$ 0.022 0.039 0.000 0.077 0.142 0.157 0.050 0.019 0.018 0.008 0.060 0.147 0.143 0.882

Consumers + CO2 (4th) + NOx 

(Med) billion 2013$ 0.029 0.054 0.000 0.111 0.191 0.217 0.069 0.027 0.024 0.011 0.080 0.199 0.197 1.207

10E-2
Table 10E.1.1 (cont.)

Units

IMHWSmallBIMHWMedBIMHWLargeBIMHASmallBRCU-

***-

LargeB

IMHALargeBSCUWLargeBSCUASmallBSCUALargeBIMHASmallCIMHALargeCRCU-

***-

SmallC

SCUASmallC

Total

Annualized Results 

Economic Impacts

Incremental Equipment Cost billion 2013$ 0.000 0.000 0.000 0.002 0.001 0.002 0.000 0.001 0.000 0.000 0.000 0.000 0.000 0.006

Operating Cost Savings billion 2013$ 0.001 0.002 0.000 0.004 0.006 0.007 0.000 0.003 0.002 0.001 0.001 0.000 0.002 0.028

NPV billion 2013$ 0.001 0.001 0.000 0.002 0.005 0.005 0.000 0.002 0.002 0.001 0.001 0.000 0.002 0.022

Emissions Savings 

(monetized)

Full-Fuel Cycle

CO2 (global)

5% dr, average million 2013$ 0.081 0.167 0.000 0.386 0.546 0.668 0.210 0.079 0.066 0.030 0.224 0.582 0.595 3.634

3% dr, average million 2013$ 0.269 0.553 0.000 1.280 1.812 2.217 0.697 0.262 0.219 0.101 0.743 1.929 1.974 12.055

2.5% dr, average million 2013$ 0.392 0.806 0.000 1.866 2.644 3.234 1.016 0.382 0.319 0.147 1.084 2.814 2.879 17.583

3% dr, 95th perc million 2013$ 0.829 1.706 0.000 3.949 5.595 6.845 2.151 0.809 0.675 0.311 2.294 5.955 6.092 37.210

NOx (3% dr)

At 476 2013$/ton million 2013$ 0.003 0.006 0.000 0.015 0.021 0.026 0.008 0.003 0.003 0.001 0.009 0.023 0.023 0.141

At 2,684 2013$/ton million 2013$ 0.018 0.036 0.000 0.084 0.120 0.146 0.046 0.017 0.014 0.007 0.049 0.127 0.130 0.796

At 4,893 2013$/ton million 2013$ 0.032 0.067 0.000 0.154 0.218 0.267 0.084 0.032 0.026 0.012 0.089 0.232 0.238 1.451

NPV

Consumer & Emissions Value 

Consumers + CO2 (1st) + NOx 

(Med) billion 2013$ 0.001 0.001 0.000 0.003 0.006 0.006 0.000 0.002 0.002 0.001 0.001 0.001 0.003 0.026

Consumers + CO2 (2nd) + NOx 

(Med) billion 2013$ 0.001 0.002 0.000 0.004 0.007 0.007 0.001 0.002 0.002 0.001 0.001 0.002 0.004 0.035

Consumers + CO2 (3rd) + NOx 

(Med) billion 2013$ 0.001 0.002 0.000 0.004 0.008 0.008 0.001 0.002 0.002 0.001 0.002 0.003 0.005 0.040

Consumers + CO2 (4th) + NOx 

(Med) billion 2013$ 0.002 0.003 0.000 0.006 0.011 0.012 0.002 0.003 0.002 0.001 0.003 0.006 0.008 0.060

10E-3
Table 10E.1.2 Cumulative and Annualized Benefits and Costs of Considered Standard Levels for ACIM Units Shipped in the 

Period 2018–2047 (TSL 2, 3 Percent Discount Rate)

Units

IMHWSmallBIMHWMedBIMHWLargeBIMHASmallBRCU-

***-

LargeB

IMHALargeBSCUWLargeBSCUASmallBSCUALargeBIMHASmallCIMHALargeCRCU-

***-

SmallC

SCUASmallC

Total

Cumulative Results 

Energy Savings

Full-Fuel Cycle quads 0.002 0.005 0.000 0.024 0.016 0.035 0.001 0.019 0.015 0.004 0.002 0.002 0.011 0.136

Economic Impacts

Incremental Equipment Cost billion 2013$ 0.006 0.008 0.000 0.103 0.014 0.067 0.001 0.034 0.028 0.005 0.001 0.002 0.009 0.276

Operating Cost Savings billion 2013$ 0.020 0.030 0.000 0.149 0.102 0.301 0.006 0.113 0.155 0.024 0.012 0.011 0.066 0.989

NPV billion 2013$ 0.014 0.022 0.000 0.046 0.088 0.234 0.005 0.079 0.127 0.019 0.011 0.009 0.057 0.712

Emissions Savings (physical)

Full-Fuel Cycle

CO2

million metric 

ton 0.146 0.301 0.000 1.478 0.987 2.160 0.913 0.240 0.119 0.110 0.647 2.004 1.074 10.179

NOx kilo-ton 0.216 0.445 0.000 2.187 1.460 3.196 1.351 0.355 0.176 0.162 0.958 2.964 1.589 15.060

Hg Ton 0.000 0.001 0.000 0.004 0.003 0.006 0.002 0.001 0.000 0.000 0.002 0.005 0.003 0.027

SO2 kilo-ton 0.124 0.255 0.000 1.252 0.836 1.830 0.773 0.203 0.101 0.093 0.548 1.697 0.910 8.621

Emissions Savings 

(monetized)

Full-Fuel Cycle

CO2 (global)

5% dr, average million 2013$ 1.076 2.212 0.000 10.868 7.255 15.885 6.714 1.764 0.875 0.806 4.760 14.732 7.899 74.845

3% dr, average million 2013$ 4.820 9.913 0.000 48.704 32.510 71.187 30.088 7.906 3.920 3.611 21.329 66.019 35.399 335.407

2.5% dr, average million 2013$ 7.618 15.667 0.000 76.975 51.381 112.510 47.553 12.495 6.196 5.707 33.710 104.342 55.948 530.102

3% dr, 95th perc million 2013$ 14.878 30.599 0.000 150.335 100.350 219.735 92.873 24.404 12.100 11.146 65.838 203.783 109.268 1035.308

NOx (3% dr)

At 476 2013$/ton million 2013$ 0.056 0.116 0.000 0.570 0.381 0.833 0.352 0.093 0.046 0.042 0.250 0.773 0.414 3.927

At 2,684 2013$/ton million 2013$ 0.318 0.655 0.000 3.216 2.147 4.701 1.987 0.522 0.259 0.238 1.408 4.359 2.337 22.147

At 4,893 2013$/ton million 2013$ 0.580 1.193 0.000 5.862 3.913 8.568 3.621 0.952 0.472 0.435 2.567 7.946 4.260 40.367

NPV

Consumer & Emissions Value 

Consumers + CO2 (1st) + NOx 

(Med) billion 2013$ 0.015 0.025 0.000 0.060 0.097 0.255 0.136 0.021 0.012 0.010 0.063 0.252 0.095 1.043

Consumers + CO2 (2nd) + NOx 

(Med) billion 2013$ 0.019 0.033 0.000 0.098 0.123 0.310 0.159 0.027 0.015 0.013 0.080 0.303 0.123 1.304

Consumers + CO2 (3rd) + NOx 

(Med) billion 2013$ 0.022 0.039 0.000 0.126 0.142 0.351 0.177 0.032 0.018 0.015 0.092 0.341 0.143 1.498

Consumers + CO2 (4th) + NOx 

(Med) billion 2013$ 0.029 0.054 0.000 0.200 0.191 0.458 0.222 0.044 0.024 0.021 0.124 0.441 0.197 2.003

10E-4
Table 10E.1.2 (cont.)

Units

IMHWSmallBIMHWMedBIMHWLargeBIMHASmallBRCU-

***-

LargeB

IMHALargeBSCUWLargeBSCUASmallBSCUALargeBIMHASmallCIMHALargeCRCU-

***-

SmallC

SCUASmallC

Total

Annualized Results 

Economic Impacts

Incremental Equipment Cost billion 2013$ 0.000 0.000 0.000 0.006 0.001 0.004 0.000 0.002 0.002 0.000 0.000 0.000 0.001 0.015

Operating Cost Savings billion 2013$ 0.001 0.002 0.000 0.008 0.006 0.017 0.000 0.006 0.009 0.001 0.001 0.001 0.004 0.055

NPV billion 2013$ 0.001 0.001 0.000 0.003 0.005 0.013 0.000 0.004 0.007 0.001 0.001 0.001 0.003 0.040

Emissions Savings 

(monetized)

Full-Fuel Cycle

CO2 (global)

5% dr, average million 2013$ 0.081 0.167 0.000 0.818 0.546 1.196 0.506 0.133 0.066 0.061 0.358 1.109 0.595 5.636

3% dr, average million 2013$ 0.269 0.553 0.000 2.715 1.812 3.969 1.677 0.441 0.219 0.201 1.189 3.681 1.974 18.699

2.5% dr, average million 2013$ 0.392 0.806 0.000 3.960 2.644 5.789 2.447 0.643 0.319 0.294 1.734 5.369 2.879 27.274

3% dr, 95th perc million 2013$ 0.829 1.706 0.000 8.381 5.595 12.250 5.178 1.361 0.675 0.621 3.670 11.361 6.092 57.719

NOx (3% dr)

At 476 2013$/ton million 2013$ 0.003 0.006 0.000 0.032 0.021 0.046 0.020 0.005 0.003 0.002 0.014 0.043 0.023 0.219

At 2,684 2013$/ton million 2013$ 0.018 0.036 0.000 0.179 0.120 0.262 0.111 0.029 0.014 0.013 0.079 0.243 0.130 1.235

At 4,893 2013$/ton million 2013$ 0.032 0.067 0.000 0.327 0.218 0.478 0.202 0.053 0.026 0.024 0.143 0.443 0.238 2.250

NPV

Consumer & Emissions Value 

Consumers + CO2 (1st) + NOx 

(Med) billion 2013$ 0.001 0.001 0.000 0.004 0.006 0.015 0.001 0.005 0.007 0.001 0.001 0.002 0.004 0.047

Consumers + CO2 (2nd) + NOx 

(Med) billion 2013$ 0.001 0.002 0.000 0.005 0.007 0.017 0.002 0.005 0.007 0.001 0.002 0.004 0.005 0.060

Consumers + CO2 (3rd) + NOx 

(Med) billion 2013$ 0.001 0.002 0.000 0.007 0.008 0.019 0.003 0.005 0.007 0.001 0.002 0.006 0.006 0.068

Consumers + CO2 (4th) + NOx 

(Med) billion 2013$ 0.002 0.003 0.000 0.011 0.011 0.026 0.006 0.006 0.008 0.002 0.004 0.012 0.009 0.099

10E-5
Table 10E.1.3 Cumulative and Annualized Benefits and Costs of Considered Standard Levels for ACIM Units Shipped in the 

Period 2018–2047 (TSL 3, 3 Percent Discount Rate)

Units

IMHWSmallBIMHWMedBIMHWLargeBIMHASmallBRCU-

***-

LargeB

IMHALargeBSCUWLargeBSCUASmallBSCUALargeBIMHASmallCIMHALargeCRCU-

***-

SmallC

SCUASmallC

Total

Cumulative Results 

Energy Savings

Full-Fuel Cycle quads 0.004 0.005 0.000 0.039 0.016 0.040 0.001 0.026 0.020 0.006 0.003 0.003 0.016 0.179

Economic Impacts

Incremental Equipment Cost billion 2013$ 0.012 0.008 0.000 0.151 0.014 0.073 0.001 0.074 0.044 0.008 0.002 0.004 0.019 0.411

Operating Cost Savings billion 2013$ 0.037 0.030 0.000 0.243 0.102 0.333 0.007 0.243 0.183 0.038 0.021 0.020 0.096 1.353

NPV billion 2013$ 0.025 0.022 0.000 0.092 0.088 0.259 0.005 0.169 0.138 0.030 0.019 0.017 0.076 0.942

Emissions Savings (physical)

Full-Fuel Cycle

CO2

million metric 

ton 0.250 0.301 0.000 2.377 0.987 2.464 1.213 0.383 0.208 0.197 0.957 2.243 1.139 12.719

NOx kilo-ton 0.370 0.445 0.000 3.517 1.460 3.646 1.794 0.566 0.308 0.292 1.417 3.318 1.685 18.817

Hg Ton 0.001 0.001 0.000 0.006 0.003 0.006 0.003 0.001 0.001 0.001 0.002 0.006 0.003 0.033

SO2 kilo-ton 0.212 0.255 0.000 2.013 0.836 2.087 1.027 0.324 0.176 0.167 0.811 1.899 0.965 10.772

Emissions Savings 

(monetized)

Full-Fuel Cycle

CO2 (global)

5% dr, average million 2013$ 1.839 2.212 0.000 17.480 7.255 18.118 8.916 2.813 1.531 1.450 7.040 16.490 8.374 93.519

3% dr, average million 2013$ 8.239 9.913 0.000 78.335 32.510 81.195 39.955 12.607 6.860 6.500 31.550 73.898 37.528 419.091

2.5% dr, average million 2013$ 13.022 15.667 0.000 123.807 51.381 128.327 63.148 19.925 10.842 10.273 49.863 116.794 59.312 662.362

3% dr, 95th perc million 2013$ 25.433 30.599 0.000 241.800 100.350 250.626 123.330 38.914 21.176 20.063 97.385 228.104 115.839 1293.617

NOx (3% dr)

At 476 2013$/ton million 2013$ 0.096 0.116 0.000 0.917 0.381 0.951 0.468 0.148 0.080 0.076 0.369 0.865 0.439 4.906

At 2,684 2013$/ton million 2013$ 0.544 0.655 0.000 5.173 2.147 5.361 2.638 0.832 0.453 0.429 2.083 4.880 2.478 27.673

At 4,893 2013$/ton million 2013$ 0.992 1.193 0.000 9.428 3.913 9.772 4.809 1.517 0.826 0.782 3.797 8.894 4.517 50.439

NPV

Consumer & Emissions Value 

Consumers + CO2 (1st) + NOx 

(Med) billion 2013$ 0.027 0.025 0.000 0.115 0.097 0.283 0.150 0.034 0.021 0.018 0.086 0.276 0.099 1.231

Consumers + CO2 (2nd) + NOx 

(Med) billion 2013$ 0.034 0.033 0.000 0.176 0.123 0.346 0.181 0.043 0.027 0.023 0.110 0.333 0.128 1.557

Consumers + CO2 (3rd) + NOx 

(Med) billion 2013$ 0.038 0.039 0.000 0.221 0.142 0.393 0.204 0.051 0.031 0.027 0.128 0.376 0.150 1.800

Consumers + CO2 (4th) + NOx 

(Med) billion 2013$ 0.051 0.054 0.000 0.339 0.191 0.515 0.264 0.070 0.041 0.037 0.176 0.487 0.207 2.432

10E-6
Table 10E.1.3 (cont.)

Units

IMHWSmallBIMHWMedBIMHWLargeBIMHASmallBRCU-

***-

LargeB

IMHALargeBSCUWLargeBSCUASmallBSCUALargeBIMHASmallCIMHALargeCRCU-

***-

SmallC

SCUASmallC

Total

Annualized Results 

Economic Impacts

Incremental Equipment Cost billion 2013$

Operating Cost Savings billion 2013$ 0.001 0.000 0.000 0.008 0.001 0.004 0.000 0.004 0.002 0.000 0.000 0.000 0.001 0.023

NPV billion 2013$ 0.002 0.002 0.000 0.014 0.006 0.019 0.000 0.014 0.010 0.002 0.001 0.001 0.005 0.075

Emissions Savings 

(monetized) 0.001 0.001 0.000 0.005 0.005 0.014 0.000 0.009 0.008 0.002 0.001 0.001 0.004 0.053

Full-Fuel Cycle

CO2 (global)

5% dr, average million 2013$

3% dr, average million 2013$ 0.138 0.167 0.000 1.316 0.546 1.364 0.671 0.212 0.115 0.109 0.530 1.242 0.631 7.042

2.5% dr, average million 2013$ 0.459 0.553 0.000 4.367 1.812 4.527 2.228 0.703 0.382 0.362 1.759 4.120 2.092 23.364

3% dr, 95th perc million 2013$ 0.670 0.806 0.000 6.370 2.644 6.603 3.249 1.025 0.558 0.529 2.566 6.009 3.052 34.079

NOx (3% dr) 1.418 1.706 0.000 13.480 5.595 13.972 6.876 2.169 1.181 1.119 5.429 12.717 6.458 72.119

At 476 2013$/ton million 2013$

At 2,684 2013$/ton million 2013$ 0.005 0.006 0.000 0.051 0.021 0.053 0.026 0.008 0.004 0.004 0.021 0.048 0.024 0.274

At 4,893 2013$/ton million 2013$ 0.030 0.036 0.000 0.288 0.120 0.299 0.147 0.046 0.025 0.024 0.116 0.272 0.138 1.543

NPV 0.055 0.067 0.000 0.526 0.218 0.545 0.268 0.085 0.046 0.044 0.212 0.496 0.252 2.812

Consumer & Emissions Value 

Consumers + CO2 (1st) + NOx 

(Med) billion 2013$

Consumers + CO2 (2nd) + NOx 

(Med) billion 2013$ 0.002 0.001 0.000 0.007 0.006 0.016 0.001 0.010 0.008 0.002 0.002 0.002 0.005 0.061

Consumers + CO2 (3rd) + NOx 

(Med) billion 2013$ 0.002 0.002 0.000 0.010 0.007 0.019 0.003 0.010 0.008 0.002 0.003 0.005 0.006 0.077

Consumers + CO2 (4th) + NOx 

(Med) billion 2013$ 0.002 0.002 0.000 0.012 0.008 0.021 0.004 0.011 0.008 0.002 0.004 0.007 0.007 0.088

10E-7
Table 10E.1.4 Cumulative and Annualized Benefits and Costs of Considered Standard Levels for ACIM Units Shipped in the 

Period 2018–2047 (TSL 4, 3 Percent Discount Rate)

Units

IMHWSmallBIMHWMedBIMHWLargeBIMHASmallBRCU-

***-

LargeB

IMHALargeBSCUWLargeBSCUASmallBSCUALargeBIMHASmallCIMHALargeCRCU-

***-

SmallC

SCUASmallC

Total

Cumulative Results 

Energy Savings

Full-Fuel Cycle quads 0.004 0.008 0.000 0.039 0.030 0.061 0.001 0.033 0.024 0.006 0.003 0.003 0.016 0.229

Economic Impacts

Incremental Equipment Cost billion 2013$ 0.012 0.036 0.000 0.151 0.101 0.189 0.006 0.125 0.178 0.008 0.002 0.004 0.019 0.831

Operating Cost Savings billion 2013$ 0.037 0.052 0.000 0.243 0.185 0.459 0.008 0.284 0.209 0.038 0.021 0.020 0.096 1.653

NPV billion 2013$ 0.025 0.016 0.000 0.092 0.084 0.271 0.002 0.159 0.031 0.030 0.019 0.017 0.076 0.822

Emissions Savings (physical)

Full-Fuel Cycle

CO2

million metric 

ton 0.250 0.505 0.000 2.377 1.825 3.726 1.463 0.383 0.208 0.197 0.957 3.504 1.978 17.374

NOx kilo-ton 0.370 0.747 0.000 3.517 2.701 5.512 2.164 0.566 0.308 0.292 1.417 5.185 2.926 25.705

Hg Ton 0.001 0.001 0.000 0.006 0.005 0.010 0.004 0.001 0.001 0.001 0.002 0.009 0.005 0.045

SO2 kilo-ton 0.212 0.428 0.000 2.013 1.546 3.155 1.239 0.324 0.176 0.167 0.811 2.968 1.675 14.714

Emissions Savings 

(monetized)

Full-Fuel Cycle

CO2 (global)

5% dr, average million 2013$ 1.839 3.714 0.000 17.480 13.422 27.395 10.757 2.813 1.531 1.450 7.040 25.767 14.542 127.750

3% dr, average million 2013$ 8.239 16.643 0.000 78.335 60.149 122.765 48.204 12.607 6.860 6.500 31.550 115.469 65.167 572.489

2.5% dr, average million 2013$ 13.022 26.304 0.000 123.807 95.064 194.027 76.186 19.925 10.842 10.273 49.863 182.495 102.994 904.804

3% dr, 95th perc million 2013$ 25.433 51.373 0.000 241.800 185.663 378.943 148.794 38.914 21.176 20.063 97.385 356.420 201.152 1767.114

NOx (3% dr)

At 476 2013$/ton million 2013$ 0.096 0.195 0.000 0.917 0.704 1.437 0.564 0.148 0.080 0.076 0.369 1.352 0.763 6.702

At 2,684 2013$/ton million 2013$ 0.544 1.099 0.000 5.173 3.972 8.106 3.183 0.832 0.453 0.429 2.083 7.624 4.303 37.802

At 4,893 2013$/ton million 2013$ 0.992 2.003 0.000 9.428 7.239 14.775 5.802 1.517 0.826 0.782 3.797 13.897 7.843 68.901

NPV

Consumer & Emissions Value 

Consumers + CO2 (1st) + NOx 

(Med) billion 2013$ 0.027 0.021 0.000 0.115 0.102 0.306 0.045 0.034 0.021 0.018 0.086 0.299 0.103 1.178

Consumers + CO2 (2nd) + NOx 

(Med) billion 2013$ 0.034 0.034 0.000 0.176 0.148 0.401 0.083 0.043 0.027 0.023 0.110 0.389 0.154 1.622

Consumers + CO2 (3rd) + NOx 

(Med) billion 2013$ 0.038 0.043 0.000 0.221 0.183 0.473 0.111 0.051 0.031 0.027 0.128 0.456 0.192 1.955

Consumers + CO2 (4th) + NOx 

(Med) billion 2013$ 0.051 0.068 0.000 0.339 0.274 0.658 0.183 0.070 0.041 0.037 0.176 0.630 0.290 2.817

10E-8
Table 10E.1.4 (cont.)

Units

IMHWSmallBIMHWMedBIMHWLargeBIMHASmallBRCU-

***-

LargeB

IMHALargeBSCUWLargeBSCUASmallBSCUALargeBIMHASmallCIMHALargeCRCU-

***-

SmallC

SCUASmallC

Total

Annualized Results 

Economic Impacts

Incremental Equipment Cost billion 2013$ 0.001 0.002 0.000 0.008 0.006 0.011 0.000 0.007 0.010 0.000 0.000 0.000 0.001 0.046

Operating Cost Savings billion 2013$ 0.002 0.003 0.000 0.014 0.010 0.026 0.000 0.016 0.012 0.002 0.001 0.001 0.005 0.092

NPV billion 2013$ 0.001 0.001 0.000 0.005 0.005 0.015 0.000 0.009 0.002 0.002 0.001 0.001 0.004 0.046

Emissions Savings 

(monetized)

Full-Fuel Cycle

CO2 (global)

5% dr, average million 2013$ 0.138 0.280 0.000 1.316 1.011 2.063 0.810 0.212 0.115 0.109 0.530 1.940 1.095 9.620

3% dr, average million 2013$ 0.459 0.928 0.000 4.367 3.353 6.844 2.687 0.703 0.382 0.362 1.759 6.437 3.633 31.916

2.5% dr, average million 2013$ 0.670 1.353 0.000 6.370 4.891 9.983 3.920 1.025 0.558 0.529 2.566 9.390 5.299 46.553

3% dr, 95th perc million 2013$ 1.418 2.864 0.000 13.480 10.351 21.126 8.295 2.169 1.181 1.119 5.429 19.870 11.214 98.517

NOx (3% dr)

At 476 2013$/ton million 2013$ 0.005 0.011 0.000 0.051 0.039 0.080 0.031 0.008 0.004 0.004 0.021 0.075 0.043 0.374

At 2,684 2013$/ton million 2013$ 0.030 0.061 0.000 0.288 0.221 0.452 0.177 0.046 0.025 0.024 0.116 0.425 0.240 2.107

At 4,893 2013$/ton million 2013$ 0.055 0.112 0.000 0.526 0.404 0.824 0.323 0.085 0.046 0.044 0.212 0.775 0.437 3.841

NPV

Consumer & Emissions Value 

Consumers + CO2 (1st) + NOx 

(Med) billion 2013$ 0.002 0.001 0.000 0.007 0.006 0.018 0.001 0.009 0.002 0.002 0.002 0.003 0.006 0.058

Consumers + CO2 (2nd) + NOx 

(Med) billion 2013$ 0.002 0.002 0.000 0.010 0.008 0.022 0.003 0.010 0.002 0.002 0.003 0.008 0.008 0.080

Consumers + CO2 (3rd) + NOx 

(Med) billion 2013$ 0.002 0.002 0.000 0.012 0.010 0.026 0.004 0.010 0.002 0.002 0.004 0.011 0.010 0.095

Consumers + CO2 (4th) + NOx 

(Med) billion 2013$ 0.003 0.004 0.000 0.019 0.015 0.037 0.009 0.011 0.003 0.003 0.007 0.021 0.016 0.146

10E-9
Table 10E.1.5 Cumulative and Annualized Benefits and Costs of Considered Standard Levels for ACIM Units Shipped in the 

Period 2018–2047 (TSL 5, 3 Percent Discount Rate)

Units

IMHWSmallBIMHWMedBIMHWLargeBIMHASmallBRCU-

***-

LargeB

IMHALargeBSCUWLargeBSCUASmallBSCUALargeBIMHASmallCIMHALargeCRCU-

***-

SmallC

SCUASmallC

Total

Cumulative Results 

Energy Savings

Full-Fuel Cycle quads 0.010 0.011 0.002 0.075 0.038 0.078 0.001 0.037 0.024 0.009 0.007 0.005 0.024 0.321

Economic Impacts

Incremental Equipment Cost billion 2013$ 0.147 0.076 0.015 0.797 0.201 0.448 0.006 0.384 0.178 0.063 0.032 0.029 0.200 2.575

Operating Cost Savings billion 2013$ 0.073 0.068 0.011 0.437 0.240 0.570 0.008 0.309 0.209 0.042 0.033 0.026 0.098 2.122

NPV billion 2013$ -0.074 -0.008 -0.003 -0.360 0.039 0.122 0.002 -0.075 0.031 -0.022 0.001 -0.002 -0.103 -0.453

Emissions Savings (physical)

Full-Fuel Cycle

CO2

million metric 

ton 0.594 0.657 0.111 4.567 2.350 4.775 1.463 0.567 0.404 0.320 1.493 4.553 2.453 24.307

NOx kilo-ton 0.879 0.972 0.165 6.757 3.476 7.064 2.164 0.839 0.598 0.473 2.209 6.737 3.628 35.962

Hg Ton 0.002 0.002 0.000 0.012 0.006 0.012 0.004 0.001 0.001 0.001 0.004 0.012 0.006 0.063

SO2 kilo-ton 0.503 0.557 0.094 3.868 1.990 4.044 1.239 0.480 0.342 0.271 1.264 3.856 2.077 20.585

Emissions Savings 

(monetized)

Full-Fuel Cycle

CO2 (global)

5% dr, average million 2013$ 4.367 4.833 0.818 33.581 17.277 35.109 10.757 4.167 2.971 2.351 10.978 33.481 18.033 178.725

3% dr, average million 2013$ 19.572 21.658 3.665 150.488 77.424 157.336 48.204 18.676 13.314 10.537 49.198 150.039 80.812 800.924

2.5% dr, average million 2013$ 30.932 34.230 5.792 237.843 122.366 248.666 76.186 29.516 21.043 16.654 77.755 237.133 127.721 1265.839

3% dr, 95th perc million 2013$ 60.412 66.853 11.313 464.516 238.985 485.653 148.794 57.647 41.098 32.525 151.859 463.130 249.445 2472.229

NOx (3% dr)

At 476 2013$/ton million 2013$ 0.229 0.254 0.043 1.762 0.906 1.842 0.564 0.219 0.156 0.123 0.576 1.757 0.946 9.377

At 2,684 2013$/ton million 2013$ 1.292 1.430 0.242 9.937 5.112 10.389 3.183 1.233 0.879 0.696 3.249 9.907 5.336 52.885

At 4,893 2013$/ton million 2013$ 2.356 2.607 0.441 18.112 9.318 18.936 5.802 2.248 1.602 1.268 5.921 18.058 9.726 96.394

NPV

Consumer & Emissions Value 

Consumers + CO2 (1st) + NOx 

(Med) billion 2013$ -0.068 -0.002 -0.002 -0.317 0.061 0.167 0.045 -0.016 0.005 0.001 -0.089 0.161 0.068 0.013

Consumers + CO2 (2nd) + NOx 

(Med) billion 2013$ -0.053 0.015 0.001 -0.200 0.121 0.290 0.083 -0.002 0.016 0.009 -0.050 0.277 0.130 0.635

Consumers + CO2 (3rd) + NOx 

(Med) billion 2013$ -0.042 0.028 0.003 -0.113 0.166 0.381 0.111 0.009 0.023 0.015 -0.022 0.364 0.177 1.100

Consumers + CO2 (4th) + NOx 

(Med) billion 2013$ -0.012 0.060 0.008 0.114 0.283 0.618 0.183 0.037 0.043 0.031 0.052 0.590 0.299 2.307

10E-10
Table 10E.1.5 (cont.)

Units

IMHWSmallBIMHWMedBIMHWLargeBIMHASmallBRCU-

***-

LargeB

IMHALargeBSCUWLargeBSCUASmallBSCUALargeBIMHASmallCIMHALargeCRCU-

***-

SmallC

SCUASmallC

Total

Annualized Results 

Economic Impacts

Incremental Equipment Cost billion 2013$ 0.008 0.004 0.001 0.044 0.011 0.025 0.000 0.021 0.010 0.004 0.002 0.002 0.011 0.144

Operating Cost Savings billion 2013$ 0.004 0.004 0.001 0.024 0.013 0.032 0.000 0.017 0.012 0.002 0.002 0.001 0.005 0.118

NPV billion 2013$ -0.004 0.000 0.000 -0.020 0.002 0.007 0.000 -0.004 0.002 -0.001 0.000 0.000 -0.006 -0.025

Emissions Savings 

(monetized)

Full-Fuel Cycle

CO2 (global)

5% dr, average million 2013$ 0.329 0.364 0.062 2.529 1.301 2.644 0.810 0.314 0.224 0.177 0.827 2.521 1.358 13.459

3% dr, average million 2013$ 1.091 1.207 0.204 8.390 4.316 8.772 2.687 1.041 0.742 0.587 2.743 8.365 4.505 44.652

2.5% dr, average million 2013$ 1.592 1.761 0.298 12.237 6.296 12.794 3.920 1.519 1.083 0.857 4.001 12.201 6.571 65.129

3% dr, 95th perc million 2013$ 3.368 3.727 0.631 25.897 13.323 27.075 8.295 3.214 2.291 1.813 8.466 25.820 13.907 137.827

NOx (3% dr)

At 476 2013$/ton million 2013$ 0.013 0.014 0.002 0.098 0.051 0.103 0.031 0.012 0.009 0.007 0.032 0.098 0.053 0.523

At 2,684 2013$/ton million 2013$ 0.072 0.080 0.013 0.554 0.285 0.579 0.177 0.069 0.049 0.039 0.181 0.552 0.297 2.948

At 4,893 2013$/ton million 2013$ 0.131 0.145 0.025 1.010 0.519 1.056 0.323 0.125 0.089 0.071 0.330 1.007 0.542 5.374

NPV

Consumer & Emissions Value 

Consumers + CO2 (1st) + NOx 

(Med) billion 2013$ -0.004 0.000 0.000 -0.017 0.004 0.010 0.001 -0.004 0.002 -0.001 0.001 0.003 -0.004 -0.009

Consumers + CO2 (2nd) + NOx 

(Med) billion 2013$ -0.003 0.001 0.000 -0.011 0.007 0.016 0.003 -0.003 0.003 -0.001 0.003 0.009 -0.001 0.022

Consumers + CO2 (3rd) + NOx 

(Med) billion 2013$ -0.002 0.001 0.000 -0.007 0.009 0.020 0.004 -0.003 0.003 0.000 0.004 0.013 0.001 0.043

Consumers + CO2 (4th) + NOx 

(Med) billion 2013$ -0.001 0.003 0.000 0.006 0.016 0.034 0.009 -0.001 0.004 0.001 0.009 0.026 0.008 0.116

10E-11
Table 10E.1.6 Cumulative and Annualized Benefits and Costs of Considered Standard Levels for ACIM Units Shipped in the 

Period 2018–2047 (TSL 1, 7 Percent Discount Rate)

Units

IMHWSmallBIMHWMedBIMHWLargeBIMHASmallBRCU-

***-

LargeB

IMHALargeBSCUWLargeBSCUASmallBSCUALargeBIMHASmallCIMHALargeCRCU-

***-

SmallC

SCUASmallC

Total

Cumulative Results 

Energy Savings

Full-Fuel Cycle quads 0.002 0.005 0.000 0.011 0.016 0.020 0.000 0.008 0.006 0.002 0.002 0.001 0.007 0.081

Economic Impacts

Incremental Equipment Cost billion 2013$ 0.003 0.004 0.000 0.015 0.007 0.016 0.000 0.007 0.005 0.001 0.001 0.000 0.002 0.063

Operating Cost Savings billion 2013$ 0.009 0.015 0.000 0.033 0.050 0.059 0.002 0.023 0.019 0.007 0.006 0.003 0.021 0.246

NPV billion 2013$ 0.006 0.010 0.000 0.017 0.042 0.043 0.002 0.016 0.014 0.006 0.005 0.002 0.018 0.183

Emissions Savings (physical)

Full-Fuel Cycle

CO2

million metric 

ton 0.146 0.301 0.000 0.697 0.987 1.207 0.379 0.143 0.119 0.055 0.405 1.050 1.074 6.562

NOx kilo-ton 0.216 0.445 0.000 1.030 1.460 1.786 0.561 0.211 0.176 0.081 0.599 1.554 1.589 9.709

Hg Ton 0.000 0.001 0.000 0.002 0.003 0.003 0.001 0.000 0.000 0.000 0.001 0.003 0.003 0.017

SO2 kilo-ton 0.124 0.255 0.000 0.590 0.836 1.022 0.321 0.121 0.101 0.046 0.343 0.889 0.910 5.558

Emissions Savings 

(monetized)

Full-Fuel Cycle

CO2 (global)

5% dr, average million 2013$ 1.076 2.212 0.000 5.121 7.255 8.876 2.789 1.049 0.875 0.403 2.975 7.722 7.899 48.251

3% dr, average million 2013$ 4.820 9.913 0.000 22.951 32.510 39.774 12.498 4.701 3.920 1.805 13.331 34.606 35.399 216.229

2.5% dr, average million 2013$ 7.618 15.667 0.000 36.273 51.381 62.862 19.753 7.430 6.196 2.854 21.069 54.695 55.948 341.745

3% dr, 95th perc million 2013$ 14.878 30.599 0.000 70.843 100.350 122.772 38.578 14.510 12.100 5.573 41.148 106.820 109.268 667.440

NOx (7% dr)

At 476 2013$/ton million 2013$ 0.029 0.059 0.000 0.136 0.193 0.236 0.074 0.028 0.023 0.011 0.079 0.206 0.210 1.284

At 2,684 2013$/ton million 2013$ 0.161 0.332 0.000 0.769 1.089 1.332 0.419 0.157 0.131 0.060 0.447 1.159 1.186 7.244

At 4,893 2013$/ton million 2013$ 0.294 0.605 0.000 1.401 1.985 2.429 0.763 0.287 0.239 0.110 0.814 2.113 2.161 13.203

NPV

Consumer & Emissions Value 

Consumers + CO2 (1st) + NOx 

(Med) billion 2013$ 0.008 0.013 0.000 0.023 0.051 0.053 0.017 0.007 0.006 0.003 0.022 0.052 0.049 0.303

Consumers + CO2 (2nd) + NOx 

(Med) billion 2013$ 0.011 0.021 0.000 0.041 0.076 0.084 0.027 0.010 0.009 0.004 0.032 0.079 0.077 0.471

Consumers + CO2 (3rd) + NOx 

(Med) billion 2013$ 0.014 0.026 0.000 0.054 0.095 0.107 0.034 0.013 0.012 0.005 0.040 0.099 0.097 0.597

Consumers + CO2 (4th) + NOx 

(Med) billion 2013$ 0.021 0.041 0.000 0.089 0.144 0.167 0.053 0.020 0.018 0.008 0.060 0.151 0.150 0.923

10E-12
Table 10E.1.6 (cont.)

Units

IMHWSmallBIMHWMedBIMHWLargeBIMHASmallBRCU-

***-

LargeB

IMHALargeBSCUWLargeBSCUASmallBSCUALargeBIMHASmallCIMHALargeCRCU-

***-

SmallC

SCUASmallC

Total

Annualized Results 

Economic Impacts

Incremental Equipment Cost billion 2013$ 0.000 0.000 0.000 0.002 0.001 0.002 0.000 0.001 0.000 0.000 0.000 0.000 0.000 0.006

Operating Cost Savings billion 2013$ 0.001 0.001 0.000 0.003 0.005 0.006 0.000 0.002 0.002 0.001 0.001 0.000 0.002 0.024

NPV billion 2013$ 0.001 0.001 0.000 0.002 0.004 0.004 0.000 0.002 0.001 0.001 0.001 0.000 0.002 0.018

Emissions Savings (monetized)

Full-Fuel Cycle

CO2 (global)

5% dr, average million 2013$ 0.081 0.167 0.000 0.386 0.546 0.668 0.210 0.079 0.066 0.030 0.224 0.582 0.595 3.634

3% dr, average million 2013$ 0.269 0.553 0.000 1.280 1.812 2.217 0.697 0.262 0.219 0.101 0.743 1.929 1.974 12.055

2.5% dr, average million 2013$ 0.392 0.806 0.000 1.866 2.644 3.234 1.016 0.382 0.319 0.147 1.084 2.814 2.879 17.583

3% dr, 95th perc million 2013$ 0.829 1.706 0.000 3.949 5.595 6.845 2.151 0.809 0.675 0.311 2.294 5.955 6.092 37.210

NOx (7% dr)

At 476 2013$/ton million 2013$ 0.003 0.006 0.000 0.013 0.019 0.023 0.007 0.003 0.002 0.001 0.008 0.020 0.021 0.127

At 2,684 2013$/ton million 2013$ 0.016 0.033 0.000 0.076 0.108 0.132 0.041 0.016 0.013 0.006 0.044 0.114 0.117 0.715

At 4,893 2013$/ton million 2013$ 0.029 0.060 0.000 0.138 0.196 0.240 0.075 0.028 0.024 0.011 0.080 0.209 0.213 1.303

NPV

Consumer & Emissions Value 

Consumers + CO2 (1st) + NOx 

(Med) billion 2013$ 0.001 0.001 0.000 0.002 0.005 0.005 0.000 0.002 0.001 0.001 0.001 0.001 0.003 0.022

Consumers + CO2 (2nd) + NOx 

(Med) billion 2013$ 0.001 0.002 0.000 0.003 0.006 0.007 0.001 0.002 0.002 0.001 0.001 0.002 0.004 0.031

Consumers + CO2 (3rd) + NOx 

(Med) billion 2013$ 0.001 0.002 0.000 0.004 0.007 0.008 0.001 0.002 0.002 0.001 0.002 0.003 0.005 0.036

Consumers + CO2 (4th) + NOx 

(Med) billion 2013$ 0.001 0.003 0.000 0.006 0.010 0.011 0.002 0.002 0.002 0.001 0.003 0.006 0.008 0.056

10E-13
Table 10E.1.7 Cumulative and Annualized Benefits and Costs of Considered Standard Levels for ACIM Units Shipped in the 

Period 2018–2047 (TSL 2, 7 Percent Discount Rate)

Units

IMHWSmallBIMHWMedBIMHWLargeBIMHASmallBRCU-

***-

LargeB

IMHALargeBSCUWLargeBSCUASmallBSCUALargeBIMHASmallCIMHALargeCRCU-

***-

SmallC

SCUASmallC

Total

Cumulative Results 

Energy Savings

Full-Fuel Cycle quads 0.002 0.005 0.000 0.024 0.016 0.035 0.001 0.019 0.015 0.004 0.002 0.002 0.011 0.136

Economic Impacts

Incremental Equipment Cost billion 2013$ 0.003 0.004 0.000 0.056 0.007 0.036 0.001 0.018 0.015 0.003 0.001 0.001 0.005 0.151

Operating Cost Savings billion 2013$ 0.009 0.015 0.000 0.073 0.050 0.145 0.003 0.055 0.074 0.012 0.006 0.005 0.032 0.479

NPV billion 2013$ 0.006 0.010 0.000 0.017 0.042 0.109 0.002 0.037 0.059 0.009 0.005 0.004 0.027 0.328

Emissions Savings (physical)

Full-Fuel Cycle

CO2

million metric 

ton 0.146 0.301 0.000 1.478 0.987 2.160 0.913 0.240 0.119 0.110 0.647 2.004 1.074 10.179

NOx kilo-ton 0.216 0.445 0.000 2.187 1.460 3.196 1.351 0.355 0.176 0.162 0.958 2.964 1.589 15.060

Hg Ton 0.000 0.001 0.000 0.004 0.003 0.006 0.002 0.001 0.000 0.000 0.002 0.005 0.003 0.027

SO2 kilo-ton 0.124 0.255 0.000 1.252 0.836 1.830 0.773 0.203 0.101 0.093 0.548 1.697 0.910 8.621

Emissions Savings (monetized)

Full-Fuel Cycle

CO2 (global)

5% dr, average million 2013$ 1.076 2.212 0.000 10.868 7.255 15.885 6.714 1.764 0.875 0.806 4.760 14.732 7.899 74.845

3% dr, average million 2013$ 4.820 9.913 0.000 48.704 32.510 71.187 30.088 7.906 3.920 3.611 21.329 66.019 35.399 335.407

2.5% dr, average million 2013$ 7.618 15.667 0.000 76.975 51.381 112.510 47.553 12.495 6.196 5.707 33.710 104.342 55.948 530.102

3% dr, 95th perc million 2013$ 14.878 30.599 0.000 150.335 100.350 219.735 92.873 24.404 12.100 11.146 65.838 203.783 109.268 1035.308

NOx (7% dr)

At 476 2013$/ton million 2013$ 0.029 0.059 0.000 0.289 0.193 0.423 0.179 0.047 0.023 0.021 0.127 0.392 0.210 1.992

At 2,684 2013$/ton million 2013$ 0.161 0.332 0.000 1.632 1.089 2.385 1.008 0.265 0.131 0.121 0.715 2.212 1.186 11.236

At 4,893 2013$/ton million 2013$ 0.294 0.605 0.000 2.974 1.985 4.347 1.837 0.483 0.239 0.220 1.302 4.031 2.161 20.480

NPV

Consumer & Emissions Value 

Consumers + CO2 (1st) + NOx 

(Med) billion 2013$ 0.008 0.013 0.000 0.029 0.051 0.127 0.067 0.011 0.006 0.005 0.033 0.126 0.049 0.524

Consumers + CO2 (2nd) + NOx 

(Med) billion 2013$ 0.011 0.021 0.000 0.067 0.076 0.182 0.090 0.017 0.009 0.008 0.049 0.177 0.077 0.785

Consumers + CO2 (3rd) + NOx 

(Med) billion 2013$ 0.014 0.026 0.000 0.095 0.095 0.223 0.108 0.022 0.012 0.010 0.062 0.215 0.097 0.979

Consumers + CO2 (4th) + NOx 

(Med) billion 2013$ 0.021 0.041 0.000 0.169 0.144 0.331 0.153 0.034 0.018 0.016 0.094 0.315 0.150 1.485

10E-14
Table 10E.1.7 (cont.)

Units

IMHWSmallBIMHWMedBIMHWLargeBIMHASmallBRCU-

***-

LargeB

IMHALargeBSCUWLargeBSCUASmallBSCUALargeBIMHASmallCIMHALargeCRCU-

***-

SmallC

SCUASmallC

Total

Annualized Results 

Economic Impacts

Incremental Equipment Cost billion 2013$ 0.000 0.000 0.000 0.006 0.001 0.004 0.000 0.002 0.001 0.000 0.000 0.000 0.000 0.015

Operating Cost Savings billion 2013$ 0.001 0.001 0.000 0.007 0.005 0.014 0.000 0.005 0.007 0.001 0.001 0.001 0.003 0.047

NPV billion 2013$ 0.001 0.001 0.000 0.002 0.004 0.011 0.000 0.004 0.006 0.001 0.001 0.000 0.003 0.032

Emissions Savings (monetized)

Full-Fuel Cycle

CO2 (global)

5% dr, average million 2013$ 0.081 0.167 0.000 0.818 0.546 1.196 0.506 0.133 0.066 0.061 0.358 1.109 0.595 5.636

3% dr, average million 2013$ 0.269 0.553 0.000 2.715 1.812 3.969 1.677 0.441 0.219 0.201 1.189 3.681 1.974 18.699

2.5% dr, average million 2013$ 0.392 0.806 0.000 3.960 2.644 5.789 2.447 0.643 0.319 0.294 1.734 5.369 2.879 27.274

3% dr, 95th perc million 2013$ 0.829 1.706 0.000 8.381 5.595 12.250 5.178 1.361 0.675 0.621 3.670 11.361 6.092 57.719

NOx (7% dr)

At 476 2013$/ton million 2013$ 0.003 0.006 0.000 0.029 0.019 0.042 0.018 0.005 0.002 0.002 0.013 0.039 0.021 0.197

At 2,684 2013$/ton million 2013$ 0.016 0.033 0.000 0.161 0.108 0.235 0.100 0.026 0.013 0.012 0.071 0.218 0.117 1.109

At 4,893 2013$/ton million 2013$ 0.029 0.060 0.000 0.294 0.196 0.429 0.181 0.048 0.024 0.022 0.129 0.398 0.213 2.022

NPV

Consumer & Emissions Value 

Consumers + CO2 (1st) + NOx 

(Med) billion 2013$ 0.001 0.001 0.000 0.003 0.005 0.012 0.001 0.004 0.006 0.001 0.001 0.002 0.003 0.039

Consumers + CO2 (2nd) + NOx 

(Med) billion 2013$ 0.001 0.002 0.000 0.005 0.006 0.015 0.002 0.004 0.006 0.001 0.002 0.004 0.005 0.052

Consumers + CO2 (3rd) + NOx 

(Med) billion 2013$ 0.001 0.002 0.000 0.006 0.007 0.017 0.003 0.004 0.006 0.001 0.002 0.006 0.006 0.061

Consumers + CO2 (4th) + NOx 

(Med) billion 2013$ 0.001 0.003 0.000 0.010 0.010 0.023 0.005 0.005 0.007 0.002 0.004 0.012 0.009 0.091

10E-15
Table 10E.1.8 Cumulative and Annualized Benefits and Costs of Considered Standard Levels for ACIM Units Shipped in the 

Period 2018–2047 (TSL 3, 7 Percent Discount Rate)

Units

IMHWSmallBIMHWMedBIMHWLargeBIMHASmallBRCU-

***-

LargeB

IMHALargeBSCUWLargeBSCUASmallBSCUALargeBIMHASmallCIMHALargeCRCU-

***-

SmallC

SCU-ASmall-C Total

Cumulative Results 

Energy Savings

Full-Fuel Cycle quads 0.004 0.005 0.000 0.039 0.016 0.040 0.001 0.026 0.020 0.006 0.003 0.003 0.016 0.179

Economic Impacts

Incremental Equipment Cost billion 2013$ 0.007 0.004 0.000 0.082 0.007 0.040 0.001 0.040 0.024 0.005 0.001 0.002 0.011 0.224

Operating Cost Savings billion 2013$ 0.018 0.015 0.000 0.119 0.050 0.160 0.003 0.117 0.088 0.019 0.010 0.010 0.047 0.654

NPV billion 2013$ 0.011 0.010 0.000 0.036 0.042 0.120 0.003 0.076 0.064 0.014 0.009 0.008 0.036 0.430

Emissions Savings (physical)

Full-Fuel Cycle

CO2

million metric 

ton 0.250 0.301 0.000 2.377 0.987 2.464 1.213 0.383 0.208 0.197 0.957 2.243 1.139 12.719

NOx kilo-ton 0.370 0.445 0.000 3.517 1.460 3.646 1.794 0.566 0.308 0.292 1.417 3.318 1.685 18.817

Hg Ton 0.001 0.001 0.000 0.006 0.003 0.006 0.003 0.001 0.001 0.001 0.002 0.006 0.003 0.033

SO2 kilo-ton 0.212 0.255 0.000 2.013 0.836 2.087 1.027 0.324 0.176 0.167 0.811 1.899 0.965 10.772

Emissions Savings (monetized)

Full-Fuel Cycle

CO2 (global)

5% dr, average million 2013$ 1.839 2.212 0.000 17.480 7.255 18.118 8.916 2.813 1.531 1.450 7.040 16.490 8.374 93.519

3% dr, average million 2013$ 8.239 9.913 0.000 78.335 32.510 81.195 39.955 12.607 6.860 6.500 31.550 73.898 37.528 419.091

2.5% dr, average million 2013$ 13.022 15.667 0.000 123.807 51.381 128.327 63.148 19.925 10.842 10.273 49.863 116.794 59.312 662.362

3% dr, 95th perc million 2013$ 25.433 30.599 0.000 241.800 100.350 250.626 123.330 38.914 21.176 20.063 97.385 228.104 115.839 1293.617

NOx (7% dr)

At 476 2013$/ton million 2013$ 0.049 0.059 0.000 0.465 0.193 0.482 0.237 0.075 0.041 0.039 0.187 0.439 0.223 2.489

At 2,684 2013$/ton million 2013$ 0.276 0.332 0.000 2.624 1.089 2.720 1.338 0.422 0.230 0.218 1.057 2.476 1.257 14.039

At 4,893 2013$/ton million 2013$ 0.503 0.605 0.000 4.783 1.985 4.958 2.440 0.770 0.419 0.397 1.926 4.512 2.291 25.590

NPV

Consumer & Emissions Value 

Consumers + CO2 (1st) + NOx 

(Med) billion 2013$ 0.013 0.013 0.000 0.056 0.051 0.141 0.074 0.017 0.011 0.010 0.044 0.138 0.051 0.620

Consumers + CO2 (2nd) + NOx 

(Med) billion 2013$ 0.020 0.021 0.000 0.117 0.076 0.204 0.105 0.027 0.016 0.015 0.069 0.196 0.080 0.945

Consumers + CO2 (3rd) + NOx 

(Med) billion 2013$ 0.024 0.026 0.000 0.163 0.095 0.251 0.128 0.035 0.020 0.018 0.087 0.238 0.102 1.189

Consumers + CO2 (4th) + NOx 

(Med) billion 2013$ 0.037 0.041 0.000 0.281 0.144 0.374 0.188 0.053 0.031 0.028 0.135 0.350 0.159 1.820

10E-16
Table 10E.1.8 (cont.)

Units

IMHWSmallBIMHWMedBIMHWLargeBIMHASmallBRCU-

***-

LargeB

IMHALargeBSCUWLargeBSCUASmallBSCUALargeBIMHASmallCIMHALargeCRCU-

***-

SmallC

SCUASmallC

Total

Annualized Results 

Economic Impacts

Incremental Equipment Cost billion 2013$ 0.001 0.000 0.000 0.008 0.001 0.004 0.000 0.004 0.002 0.000 0.000 0.000 0.001 0.022

Operating Cost Savings billion 2013$ 0.002 0.001 0.000 0.012 0.005 0.016 0.000 0.012 0.009 0.002 0.001 0.001 0.005 0.065

NPV billion 2013$ 0.001 0.001 0.000 0.004 0.004 0.012 0.000 0.008 0.006 0.001 0.001 0.001 0.004 0.042

Emissions Savings (monetized)

Full-Fuel Cycle

CO2 (global)

5% dr, average million 2013$ 0.138 0.167 0.000 1.316 0.546 1.364 0.671 0.212 0.115 0.109 0.530 1.242 0.631 7.042

3% dr, average million 2013$ 0.459 0.553 0.000 4.367 1.812 4.527 2.228 0.703 0.382 0.362 1.759 4.120 2.092 23.364

2.5% dr, average million 2013$ 0.670 0.806 0.000 6.370 2.644 6.603 3.249 1.025 0.558 0.529 2.566 6.009 3.052 34.079

3% dr, 95th perc million 2013$ 1.418 1.706 0.000 13.480 5.595 13.972 6.876 2.169 1.181 1.119 5.429 12.717 6.458 72.119

NOx (7% dr)

At 476 2013$/ton million 2013$ 0.005 0.006 0.000 0.046 0.019 0.048 0.023 0.007 0.004 0.004 0.018 0.043 0.022 0.246

At 2,684 2013$/ton million 2013$ 0.027 0.033 0.000 0.259 0.108 0.269 0.132 0.042 0.023 0.021 0.104 0.244 0.124 1.386

At 4,893 2013$/ton million 2013$ 0.050 0.060 0.000 0.472 0.196 0.489 0.241 0.076 0.041 0.039 0.190 0.445 0.226 2.526

NPV

Consumer & Emissions Value 

Consumers + CO2 (1st) + NOx 

(Med) billion 2013$ 0.001 0.001 0.000 0.005 0.005 0.014 0.001 0.008 0.006 0.002 0.002 0.002 0.004 0.051

Consumers + CO2 (2nd) + NOx 

(Med) billion 2013$ 0.002 0.002 0.000 0.008 0.006 0.017 0.003 0.008 0.007 0.002 0.003 0.005 0.006 0.067

Consumers + CO2 (3rd) + NOx 

(Med) billion 2013$ 0.002 0.002 0.000 0.010 0.007 0.019 0.004 0.009 0.007 0.002 0.004 0.007 0.007 0.078

Consumers + CO2 (4th) + NOx 

(Med) billion 2013$ 0.003 0.003 0.000 0.017 0.010 0.026 0.007 0.010 0.007 0.003 0.006 0.014 0.010 0.116

10E-17
Table 10E.1.9 Cumulative and Annualized Benefits and Costs of Considered Standard Levels for ACIM Units Shipped in the 

Period 2018–2047 (TSL 4, 7 Percent Discount Rate)

Units

IMHWSmallBIMHWMedBIMHWLargeBIMHASmallBRCU-

***-

LargeB

IMHALargeBSCUWLargeBSCUASmallBSCUALargeBIMHASmallCIMHALargeCRCU-

***-

SmallC

SCU-ASmall-C Total

Cumulative Results 

Energy Savings

Full-Fuel Cycle quads 0.004 0.008 0.000 0.039 0.030 0.061 0.001 0.033 0.024 0.006 0.003 0.003 0.016 0.229

Economic Impacts

Incremental Equipment Cost billion 2013$ 0.007 0.019 0.000 0.082 0.055 0.114 0.003 0.068 0.097 0.005 0.001 0.002 0.011 0.463

Operating Cost Savings billion 2013$ 0.018 0.025 0.000 0.119 0.090 0.222 0.004 0.136 0.101 0.019 0.010 0.010 0.047 0.801

NPV billion 2013$ 0.011 0.006 0.000 0.036 0.035 0.109 0.001 0.068 0.004 0.014 0.009 0.008 0.036 0.337

Emissions Savings (physical)

Full-Fuel Cycle

CO2

million metric 

ton 0.250 0.505 0.000 2.377 1.825 3.726 1.463 0.383 0.208 0.197 0.957 3.504 1.978 17.374

NOx kilo-ton 0.370 0.747 0.000 3.517 2.701 5.512 2.164 0.566 0.308 0.292 1.417 5.185 2.926 25.705

Hg Ton 0.001 0.001 0.000 0.006 0.005 0.010 0.004 0.001 0.001 0.001 0.002 0.009 0.005 0.045

SO2 kilo-ton 0.212 0.428 0.000 2.013 1.546 3.155 1.239 0.324 0.176 0.167 0.811 2.968 1.675 14.714

Emissions Savings (monetized)

Full-Fuel Cycle

CO2 (global)

5% dr, average million 2013$ 1.839 3.714 0.000 17.480 13.422 27.395 10.757 2.813 1.531 1.450 7.040 25.767 14.542 127.750

3% dr, average million 2013$ 8.239 16.643 0.000 78.335 60.149 122.765 48.204 12.607 6.860 6.500 31.550 115.469 65.167 572.489

2.5% dr, average million 2013$ 13.022 26.304 0.000 123.807 95.064 194.027 76.186 19.925 10.842 10.273 49.863 182.495 102.994 904.804

3% dr, 95th perc million 2013$ 25.433 51.373 0.000 241.800 185.663 378.943 148.794 38.914 21.176 20.063 97.385 356.420 201.152 1767.114

NOx (7% dr)

At 476 2013$/ton million 2013$ 0.049 0.099 0.000 0.465 0.357 0.729 0.286 0.075 0.041 0.039 0.187 0.686 0.387 3.400

At 2,684 2013$/ton million 2013$ 0.276 0.558 0.000 2.624 2.015 4.113 1.615 0.422 0.230 0.218 1.057 3.868 2.183 19.178

At 4,893 2013$/ton million 2013$ 0.503 1.016 0.000 4.783 3.673 7.496 2.943 0.770 0.419 0.397 1.926 7.051 3.979 34.956

NPV

Consumer & Emissions Value 

Consumers + CO2 (1st) + NOx 

(Med) billion 2013$ 0.013 0.010 0.000 0.056 0.051 0.140 0.016 0.017 0.011 0.010 0.044 0.137 0.051 0.557

Consumers + CO2 (2nd) + NOx 

(Med) billion 2013$ 0.020 0.023 0.000 0.117 0.097 0.235 0.054 0.027 0.016 0.015 0.069 0.227 0.102 1.002

Consumers + CO2 (3rd) + NOx 

(Med) billion 2013$ 0.024 0.033 0.000 0.163 0.132 0.307 0.082 0.035 0.020 0.018 0.087 0.294 0.140 1.334

Consumers + CO2 (4th) + NOx 

(Med) billion 2013$ 0.037 0.058 0.000 0.281 0.223 0.492 0.154 0.053 0.031 0.028 0.135 0.468 0.238 2.197

10E-18
Table 10E.1.9 (cont.)

Units

IMHWSmallBIMHWMedBIMHWLargeBIMHASmallBRCU-

***-

LargeB

IMHALargeBSCUWLargeBSCUASmallBSCUALargeBIMHASmallCIMHALargeCRCU-

***-

SmallC

SCUASmallC

Total

Annualized Results 

Economic Impacts

Incremental Equipment Cost billion 2013$ 0.001 0.002 0.000 0.008 0.005 0.011 0.000 0.007 0.010 0.000 0.000 0.000 0.001 0.046

Operating Cost Savings billion 2013$ 0.002 0.002 0.000 0.012 0.009 0.022 0.000 0.013 0.010 0.002 0.001 0.001 0.005 0.079

NPV billion 2013$ 0.001 0.001 0.000 0.004 0.003 0.011 0.000 0.007 0.000 0.001 0.001 0.001 0.004 0.033

Emissions Savings (monetized)

Full-Fuel Cycle

CO2 (global)

5% dr, average million 2013$ 0.138 0.280 0.000 1.316 1.011 2.063 0.810 0.212 0.115 0.109 0.530 1.940 1.095 9.620

3% dr, average million 2013$ 0.459 0.928 0.000 4.367 3.353 6.844 2.687 0.703 0.382 0.362 1.759 6.437 3.633 31.916

2.5% dr, average million 2013$ 0.670 1.353 0.000 6.370 4.891 9.983 3.920 1.025 0.558 0.529 2.566 9.390 5.299 46.553

3% dr, 95th perc million 2013$ 1.418 2.864 0.000 13.480 10.351 21.126 8.295 2.169 1.181 1.119 5.429 19.870 11.214 98.517

NOx (7% dr)

At 476 2013$/ton million 2013$ 0.005 0.010 0.000 0.046 0.035 0.072 0.028 0.007 0.004 0.004 0.018 0.068 0.038 0.336

At 2,684 2013$/ton million 2013$ 0.027 0.055 0.000 0.259 0.199 0.406 0.159 0.042 0.023 0.021 0.104 0.382 0.216 1.893

At 4,893 2013$/ton million 2013$ 0.050 0.100 0.000 0.472 0.363 0.740 0.291 0.076 0.041 0.039 0.190 0.696 0.393 3.451

NPV

Consumer & Emissions Value 

Consumers + CO2 (1st) + NOx 

(Med) billion 2013$ 0.001 0.001 0.000 0.005 0.005 0.013 0.001 0.007 0.001 0.002 0.002 0.003 0.005 0.045

Consumers + CO2 (2nd) + NOx 

(Med) billion 2013$ 0.002 0.002 0.000 0.008 0.007 0.018 0.003 0.007 0.001 0.002 0.003 0.008 0.007 0.067

Consumers + CO2 (3rd) + NOx 

(Med) billion 2013$ 0.002 0.002 0.000 0.010 0.009 0.021 0.004 0.008 0.001 0.002 0.004 0.011 0.009 0.082

Consumers + CO2 (4th) + NOx 

(Med) billion 2013$ 0.003 0.003 0.000 0.017 0.014 0.032 0.009 0.009 0.002 0.003 0.006 0.021 0.015 0.134

10E-19
Table 10E.1.10 Cumulative and Annualized Benefits and Costs of Considered Standard Levels for ACIM Units Shipped in the 

Period 2018–2047 (TSL 5, 7 Percent Discount Rate)

Units

IMHWSmallBIMHWMedBIMHWLargeBIMHASmallBRCU-

***-

LargeB

IMHALargeBSCUWLargeBSCUASmallBSCUALargeBIMHASmallCIMHALargeCRCU-

***-

SmallC

SCUASmallC

Total

Cumulative Results 

Energy Savings

Full-Fuel Cycle quads 0.010 0.011 0.002 0.075 0.038 0.078 0.001 0.037 0.024 0.009 0.007 0.005 0.024 0.321

Economic Impacts

Incremental Equipment Cost billion 2013$ 0.085 0.041 0.008 0.452 0.110 0.255 0.003 0.209 0.097 0.035 0.017 0.016 0.109 1.435

Operating Cost Savings billion 2013$ 0.035 0.033 0.006 0.214 0.117 0.276 0.004 0.149 0.101 0.020 0.016 0.013 0.047 1.030

NPV billion 2013$ -0.049 -0.008 -0.002 -0.238 0.007 0.021 0.001 -0.060 0.004 -0.014 -0.001 -0.003 -0.062 -0.406

Emissions Savings (physical)

Full-Fuel Cycle

CO2

million metric 

ton 0.594 0.657 0.111 4.567 2.350 4.775 1.463 0.567 0.404 0.320 1.493 4.553 2.453 24.307

NOx kilo-ton 0.879 0.972 0.165 6.757 3.476 7.064 2.164 0.839 0.598 0.473 2.209 6.737 3.628 35.962

Hg Ton 0.002 0.002 0.000 0.012 0.006 0.012 0.004 0.001 0.001 0.001 0.004 0.012 0.006 0.063

SO2 kilo-ton 0.503 0.557 0.094 3.868 1.990 4.044 1.239 0.480 0.342 0.271 1.264 3.856 2.077 20.585

Emissions Savings (monetized)

Full-Fuel Cycle

CO2 (global)

5% dr, average million 2013$ 4.367 4.833 0.818 33.581 17.277 35.109 10.757 4.167 2.971 2.351 10.978 33.481 18.033 178.725

3% dr, average million 2013$ 19.572 21.658 3.665 150.488 77.424 157.336 48.204 18.676 13.314 10.537 49.198 150.039 80.812 800.924

2.5% dr, average million 2013$ 30.932 34.230 5.792 237.843 122.366 248.666 76.186 29.516 21.043 16.654 77.755 237.133 127.721 1265.839

3% dr, 95th perc million 2013$ 60.412 66.853 11.313 464.516 238.985 485.653 148.794 57.647 41.098 32.525 151.859 463.130 249.445 2472.229

NOx (7% dr)

At 476 2013$/ton million 2013$ 0.116 0.129 0.022 0.894 0.460 0.935 0.286 0.111 0.079 0.063 0.292 0.891 0.480 4.757

At 2,684 2013$/ton million 2013$ 0.656 0.726 0.123 5.041 2.594 5.271 1.615 0.626 0.446 0.353 1.648 5.026 2.707 26.831

At 4,893 2013$/ton million 2013$ 1.195 1.322 0.224 9.189 4.727 9.607 2.943 1.140 0.813 0.643 3.004 9.161 4.934 48.904

NPV

Consumer & Emissions Value 

Consumers + CO2 (1st) + NOx 

(Med) billion 2013$ -0.044 -0.003 -0.002 -0.199 0.027 0.062 0.016 -0.010 0.002 0.000 -0.049 0.059 0.030 -0.111

Consumers + CO2 (2nd) + NOx 

(Med) billion 2013$ -0.029 0.014 0.001 -0.083 0.087 0.184 0.054 0.005 0.013 0.008 -0.011 0.175 0.093 0.512

Consumers + CO2 (3rd) + NOx 

(Med) billion 2013$ -0.018 0.027 0.003 0.005 0.132 0.275 0.082 0.016 0.020 0.014 0.017 0.262 0.140 0.977

Consumers + CO2 (4th) + NOx 

(Med) billion 2013$ 0.012 0.059 0.009 0.231 0.249 0.512 0.154 0.044 0.040 0.030 0.092 0.488 0.262 2.183

10E-20
Table 10E.1.10 (cont.)

Units

IMHWSmallBIMHWMedBIMHWLargeBIMHASmallBRCU-

***-

LargeB

IMHALargeBSCUWLargeBSCUASmallBSCUALargeBIMHASmallCIMHALargeCRCU-

***-

SmallC

SCUASmallC

Total

Annualized Results 

Economic Impacts

Incremental Equipment Cost billion 2013$ 0.008 0.004 0.001 0.045 0.011 0.025 0.000 0.021 0.010 0.003 0.002 0.002 0.011 0.142

Operating Cost Savings billion 2013$ 0.003 0.003 0.001 0.021 0.012 0.027 0.000 0.015 0.010 0.002 0.002 0.001 0.005 0.102

NPV billion 2013$ -0.005 -0.001 0.000 -0.024 0.001 0.002 0.000 -0.006 0.000 -0.001 0.000 0.000 -0.006 -0.040

Emissions Savings (monetized)

Full-Fuel Cycle

CO2 (global)

5% dr, average million 2013$ 0.329 0.364 0.062 2.529 1.301 2.644 0.810 0.314 0.224 0.177 0.827 2.521 1.358 13.459

3% dr, average million 2013$ 1.091 1.207 0.204 8.390 4.316 8.772 2.687 1.041 0.742 0.587 2.743 8.365 4.505 44.652

2.5% dr, average million 2013$ 1.592 1.761 0.298 12.237 6.296 12.794 3.920 1.519 1.083 0.857 4.001 12.201 6.571 65.129

3% dr, 95th perc million 2013$ 3.368 3.727 0.631 25.897 13.323 27.075 8.295 3.214 2.291 1.813 8.466 25.820 13.907 137.827

NOx (7% dr)

At 476 2013$/ton million 2013$ 0.011 0.013 0.002 0.088 0.045 0.092 0.028 0.011 0.008 0.006 0.029 0.088 0.047 0.470

At 2,684 2013$/ton million 2013$ 0.065 0.072 0.012 0.498 0.256 0.520 0.159 0.062 0.044 0.035 0.163 0.496 0.267 2.649

At 4,893 2013$/ton million 2013$ 0.118 0.131 0.022 0.907 0.467 0.948 0.291 0.113 0.080 0.064 0.297 0.904 0.487 4.828

NPV

Consumer & Emissions Value 

Consumers + CO2 (1st) + NOx 

(Med) billion 2013$ -0.004 0.000 0.000 -0.020 0.002 0.005 0.001 -0.006 0.001 -0.001 0.001 0.003 -0.004 -0.024

Consumers + CO2 (2nd) + NOx 

(Med) billion 2013$ -0.004 0.000 0.000 -0.015 0.005 0.011 0.003 -0.005 0.001 -0.001 0.003 0.009 -0.001 0.007

Consumers + CO2 (3rd) + NOx 

(Med) billion 2013$ -0.003 0.001 0.000 -0.011 0.007 0.015 0.004 -0.004 0.002 -0.001 0.004 0.012 0.001 0.028

Consumers + CO2 (4th) + NOx 

(Med) billion 2013$ -0.001 0.003 0.000 0.003 0.014 0.030 0.009 -0.003 0.003 0.000 0.009 0.026 0.008 0.100

10E-21
CHAPTER 11. CUSTOMER SUBGROUP ANALYSIS

TABLE OF CONTENTS

11.1 INTRODUCTION ............................................................................................................ 11-1

11.2 IDENTIFYING THE CUSTOMER SUBGROUPS......................................................... 11-1

11.3 LIFE-CYCLE COST AND PAYBACK PERIOD RESULTS FOR SMALL BUSINESS 

SUBGROUPS................................................................................................................... 11-7

REFERENCES ........................................................................................................................ 11-14

LIST OF TABLES

Table 11.2.1 Business Types Purchasing Automatic Commercial Ice Makers......................... 11-1

Table 11.2.2 SBA Size Standards for Businesses that Use Automatic Commercial ice 

Makers................................................................................................................ 11-2

Table 11.2.3 Single-Unit and Multiunit Firms Census Data for Supermarket and Other 

Grocery (Except Convenience) Stores............................................................... 11-3

Table 11.2.4 Single-Unit and Multiunit Firms Census Data for Convenience Stores............... 11-4

Table 11.2.5 Single-Unit and Multiunit Firms Census Data for Gasoline Stations with 

Convenience Stores............................................................................................ 11-4

Table 11.2.6 Single-Unit and Multiunit Firms Census Data for Full-Service Restaurants ....... 11-5

Table 11.2.7 Single-Unit and Multiunit Firms Census Data for Limited-Service 

Restaurants......................................................................................................... 11-5

Table 11.2.8 Single-Unit and Multiunit Firms Census Data for Lodging* ............................... 11-6

Table 11.3.1 Comparison of Mean LCC Savings for the Small Business Subgroup in the 

Foodservice Sector with the National Average Values ..................................... 11-8

Table 11.3.2 Percentage Change in Mean LCC Savings for the Small Business Subgroup 

in the Foodservice Sector Compared to National Average Values*.................. 11-9

Table 11.3.3 Comparison of Median Payback Periods for the Small Business Subgroup in 

the Foodservice Sector with National Median Values..................................... 11-10

Table 11.3.4 Comparison of LCC Savings for the Small Business Subgroup in the 

Lodging Sector with the National Average Values ......................................... 11-11

Table 11.3.5 Percentage Change in the Mean LCC Savings for the Small Business 

Subgroup in the Lodging Sector Compared to the National Average 

Values .............................................................................................................. 11-12

Table 11.3.6 Comparison of Median Payback Periods for the Small Business Subgroup in 

the Lodging Sector with the National Median Values..................................... 11-13

11-i
CHAPTER 11. CUSTOMER SUBGROUP ANALYSIS

11.1 INTRODUCTION

The life-cycle cost (LCC) customer subgroup analysis evaluates impacts on identifiable 

groups of customers of automatic commercial ice-making (ACIM) equipment who may be 

disproportionately affected by amended energy conservation standards. The LCC and payback 

period (PBP) analysis described in chapter 8 of the technical support document (TSD) is applied 

to seven major business or building types that use a majority of the ACIM equipment—health 

care, lodging, foodservice, retail, food sales, education, and office. Although the inputs for 

different types of businesses are different in the LCC and PBP analysis, the final results may not 

reflect the results experienced by certain customer subgroups. In other words, some of the

adverse impacts on businesses that are disproportionately disadvantaged may be masked by the 

averaging effect of the LCC and PBP analysis. Therefore, the U.S. Department of Energy (DOE) 

carried out the LCC subgroup analysis by using the LCC and PBP analysis spreadsheet, but 

applying the inputs that are applicable only to the identified subgroups. The LCC spreadsheet 

model is accessible at www1.eere.energy.gov/buildings/appliance_standards/rulemaking.aspx/

ruleid/29.

11.2 IDENTIFYING THE CUSTOMER SUBGROUPS

DOE identified small businesses as a subgroup that could potentially be affected 

disproportionately by the amended energy conservation standard for automatic commercial ice 

makers. DOE was concerned that increases in the purchase price of ACIM equipment could have 

negative impacts on small businesses (i.e., those with low annual revenues). Table 11.2.1 lists the 

business types that typically purchase automatic commercial ice makers.

Table 11.2.1 Business Types Purchasing Automatic Commercial Ice Makers

Business Type Business Type as Percent

of ACIM Stock

Health Care 9%

Lodging 33%

Foodservice 22%

Retail 8%

Education 7%

Food Sales 16%

Office 4%

Total 100%

One category, office, is not a specific category that can be associated with specific small 

business definitions insofar as it includes a wide range of industries. In the case of education, 

based on available information, DOE estimates that 75 percent of educational establishments are 

owned by state and local governmental entities.1 Similarly, DOE estimates that almost 80 

percent of hospitals are owned by governmental entities.2 While it is clearly possible for a school 

or hospital district to be disadvantaged, given the ability to levy taxes, special-purpose districts 

have resources not available to small business like family-owned grocery stores. A fourth 

category, retail, includes the so-called “big box” retailers that sell everything from beds, to 

11-1
automotive supplies, to medicines and optical services, to food, and beyond. Retail also includes 

low-end retailers and retailers specializing in a wide range of goods obtained as close-out or 

discontinued products and sold inexpensively. While it is possible for single-establishment firms 

to be in these business lines, DOE views this business category as one dominated by regional or 

larger chains, and modeled it thusly in the calculation of discount rates. (See chapter 8 of the 

TSD for the development of discount rates.) For these reasons, DOE confined the research for 

identifying small businesses to the remaining three business types—lodging, foodservice, and 

food sales.

To identify small businesses, DOE used size standards from the Small Business 

Administration (SBA) to define which business entities are considered to be small. The SBA 

established size standards for types of economic activity, or industry, under the North American 

Industry Classification System (NAICS).3 Table 11.2.2 presents the size standards established by 

SBA for various businesses that use ACIM equipment.

Table 11.2.2 SBA Size Standards for Businesses that Use Automatic Commercial ice 

Makers

Business Type

SBA Size Standard 

Annual Sales 

millions

Supermarkets and Other Grocery (except 

Convenience) Stores $30.0 

Convenience Stores $27.0 

Meat Markets $7.0 

Fish and Seafood Markets $7.0 

All Other Specialty Food Stores $7.0 

Beer, Wine and Liquor Stores $7.0 

Gasoline Stations with Convenience Stores $27.0 

Warehouse Clubs and Superstores $27.0 

Hotels (except Casino Hotels) and Motels $30.0 

Casino Hotels $30.0 

Full-Service Restaurants $7.0 

Limited-Service Restaurants $10.0 

Cafeterias, Grill Buffets, and Buffets $25.5 

Snack and Nonalcoholic Beverage Bars $7.0 

Food Service Contractors $35.5 

Caterers $7.0 

Mobile Food Services $7.0 

Drinking Places (Alcoholic Beverages) $7.0 

Source: Size Standards Used To Define Small Business Concerns (13 CFR 121.201); 

also available at www.sba.gov/content/table-small-business-size-standards. (Last 

accessed on April 17, 2013.)

In examining the businesses that purchase and use automatic commercial ice makers, 

DOE analyzed detailed statistical data from the 2007 economic census.

4 Table 11.2.3, 

Table 11.2.4, and Table 11.2.5 present the census data for single-unit and multiunit firms for 

three different types of businesses that constitute a dominant share of the ACIM equipment 

market.

Table 11.2.3 presents the data for supermarkets and other grocery (except convenience) 

stores. The data show that 98.9 percent of the firms in this category fall under the SBA definition 

11-2
of small business concerns and represent 66 percent of the total number of establishments, 

23 percent of employment, and 18 percent of sales. 

Table 11.2.3 Single-Unit and Multiunit Firms Census Data for Supermarket and Other 

Grocery (Except Convenience) Stores

Description Number 

of Firms

Total 

Employment

Number of 

Establishments

Sales per 

Firm 

1000$

Is Average 

Firm a 

Small 

Business?

Supermarkets & other grocery 

(except convenience) stores*

All firms 41,885 2,432,425 64,881 11,131 Yes

Single-unit firms 39,878 438,104 39,878 1,676 Yes

Multiunit firms 2,007 1,994,321 25,003 198,998 No

Firms with 1 establishment 577 18,882 577 4,481 Yes

Firms with 2 establishments 597 49,967 1,194 11,824 Yes

Firms with 3 or 4 establishments 374 62,813 1,233 25,250 Yes

Firms with 5 to 9 establishments 235 87,447 1,514 64,030 No

Firms with 10 to 24 establishments 128 125,436 1,797 178,695 No

Firms with 25 to 49 establishments 38 88,107 1,262 422,285 No

Firms with 50 to 99 establishments 22 133,964 1,533 1,130,223 No

Firms with 100 establishments or 

more

36 1,427,705 15,893 8,374,159 No

Fraction of firms classed as small business 0.989

Fraction of establishments that belong to firms 

classified as small businesses 0.661

Fraction of employment in small businesses 0.234

Fraction of sales in small businesses 0.184

*Small business concerns in this category are firms with annual sales less than $30 million.

Table 11.2.4 presents the data for convenience stores. The data show that 99.9 percent of 

the firms in this category fall under the SBA definition of small business concerns and represent

90 percent of the total number of establishments, 77 percent of employment, and 76 percent of 

sales.

11-3
Table 11.2.4 Single-Unit and Multiunit Firms Census Data for Convenience Stores

Description Number 

of Firms

Total 

Employment

Number of 

Establishments

Sales per 

Firm 

1000$

Is Average 

Firm a 

Small 

Business?

Convenience Stores*

All firms 22,168 118,787 25,510 942 Yes

Single-unit firms 21,529 79,302 21,529 661 Yes

Multiunit firms 639 39,485 3,981 10,408 Yes

Firms with 1 establishment 300 2,458 300 1,087 Yes

Firms with 2 establishments 183 2,804 366 2,318 Yes

Firms with 3 or 4 establishments 86 2,251 281 4,111 Yes

Firms with 5 to 9 establishments 25 1,014 151 5,843 Yes

Firms with 10 to 24 establishments 19 3,369 302 23,634 Yes

Firms with 25 to 49 establishments 12 3,320 443 45,925 No

Firms with 50 to 99 establishments 10 6,365 694 116,707 No

Firms with 100 establishments or 

more

4 17,904 1,444 808,383 No

Fraction of firms classed as small business 0.999

Fraction of establishments that belong to firms 

classified as small businesses 0.899

Fraction of employment in small businesses 0.768

Fraction of sales in small businesses 0.763

*Small business concerns in this category are firms with annual sales less than $27 million.

Table 11.2.5 presents the data for gasoline stations with convenience stores. The data 

show that 98.8 percent of the firms in this category fall under the SBA definition of small 

business concerns and represent 60 percent of the total number of establishments, 52 percent of 

employment, and 43 percent of sales.

Table 11.2.5 Single-Unit and Multiunit Firms Census Data for Gasoline Stations with 

Convenience Stores

Description Number 

of Firms

Total 

Employment

Number of 

Establishments

Sales per 

Firm 

1000$

Is Average 

Firm a 

Small 

Business?

Gasoline Stations with Convenience Stores*

All firms 53,375 719,108 97,508 6,300 Yes

Single-unit firms 49,010 287,220 49,010 2,249 Yes

Multiunit firms 4,365 431,888 48,498 51,788 No

Firms with 1 establishment 1,160 11,836 1,160 3,363 Yes

Firms with 2 establishments 1,168 21,386 2,336 6,622 Yes

Firms with 3 or 4 establishments 802 24,023 2,697 11,713 Yes

Firms with 5 to 9 establishments 573 30,488 3,709 22,945 Yes

Firms with 10 to 24 establishments 402 47,037 5,978 51,678 No

Firms with 25 to 49 establishments 150 45,235 5,020 128,070 No

Firms with 50 to 99 establishments 58 31,187 3,865 272,955 No

Firms with 100 establishments or 

more

52 220,696 23,733 2,616,567 No

Fraction of firms classed as small business 0.988

Fraction of establishments that belong to firms 

classified as small businesses 0.604

Fraction of employment in small businesses 0.521

Fraction of sales in small businesses 0.429

*Small business concerns in this category are firms with annual sales less than $27 million.

11-4
Table 11.2.6 presents the data for full-service restaurants. The data show that 

99.5 percent of the firms in this category fall under the SBA definition of small business 

concerns and represent 87 percent of the total number of establishments, 64 percent of 

employment, and 65 percent of sales.5

Table 11.2.6 Single-Unit and Multiunit Firms Census Data for Full-Service Restaurants

Description Number 

of Firms

Total 

Employment

Number of 

Establishments

Sales per 

Firm 

1000$

Is Average 

Firm a 

Small 

Business?

Full-service Restaurants*

All firms 188,758 4,603,747 220,089 1,019 Yes

Single-unit firms 183,759 2,633,075 183,759 614 Yes

Multiunit firms 4,999 1,970,672 36,330 15,880 No

Firms with 1 establishment 1,477 57,098 1,477 1,696 Yes

Firms with 2 establishments 1,762 119,803 3,524 2,797 Yes

Firms with 3 or 4 establishments 833 115,801 2,796 5,638 Yes

Firms with 5 to 9 establishments 455 142,324 2,966 12,156 No

Firms with 10 establishments or 

more

472 1,535,646 25,567 130,770 No

Fraction of firms classed as small business 0.995

Fraction of establishments that belong to firms 

classified as small businesses 0.870

Fraction of employment in small businesses 0.636

Fraction of sales in small businesses 0.650

*Small business concerns in this category are firms with annual sales less than $7 million.

Table 11.2.7 presents the data for limited-service restaurants. The data show that 

99 percent of the firms in this category fall under the SBA definition of small business concerns 

and represent 73 percent of the total number of establishments, 58 percent of employment, and 

58 percent of sales.

Table 11.2.7 Single-Unit and Multiunit Firms Census Data for Limited-Service Restaurants

Description Number 

of Firms

Total 

Employment

Number of 

Establishments

Sales per 

Firm 

1000$

Is Average 

Firm a 

Small 

Business?

Limited-service Restaurants*

All firms 136,505 3,384,517 211,313 1,109 Yes

Single-unit firms 126,341 1,263,854 126,341 447 Yes

Multiunit firms 10,164 2,120,663 84,972 9,331 Yes

Firms with 1 establishment 1,271 27,674 1,271 981 Yes

Firms with 2 establishments 2,821 143,768 5,642 2,151 Yes

Firms with 3 or 4 establishments 2,717 221,095 9,174 3,460 Yes

Firms with 5 to 9 establishments 1,934 319,625 12,391 7,154 Yes

Firms with 10 establishments or 

more 1,421 1,408,501 56,494 45,239 No

Fraction of firms classed as small business 0.990

Fraction of establishments that belong to firms 

classified as small businesses 0.733

Fraction of employment in small businesses 0.584

Fraction of sales in small businesses 0.575

*Small business concerns in this category are firms with annual sales less than $10 million.

11-5
Table 11.2.8 presents the data for lodging. The data show that over 99 percent of the 

firms in this category fall under the SBA definition of small business concerns and represent 85 

percent of the total number of establishments, 56 percent of employment, and 50 percent of sales.

Table 11.2.8 Single-Unit and Multiunit Firms Census Data for Lodging*

Description Number 

of Firms

Total 

Employment

Number of 

Establishments

Sales per 

Firm 

1000$

Is Average 

Firm a 

Small 

Business?

Limited-service Restaurants*

All firms 52,001 1,970,661 62,740 3,469 Yes

Single-unit firms 49,340 813,879 49,340 1,277 Yes

Multiunit firms 2,661 1,156,782 13,400 44,104 No

Firms with 1 establishment 1,142 106,804 1,142 8,588 Yes

Firms with 2 establishments 770 102,044 1,540 12,778 Yes

Firms with 3 or 4 establishments 385 86,904 1,275 19,890 Yes

Firms with 5 to 9 establishments 194 121,124 1,259 64,389 No

Firms with 10 establishments or 

more

170 739,906 8,184 456,255 No

Fraction of firms classed as small business 0.993

Fraction of establishments that belong to firms 

classified as small businesses 0.849

Fraction of employment in small businesses 0.563

Fraction of sales in small businesses 0.501

*Lodging is referred to as Accommodations and food service in this particular database. Small business concerns in this 

category are firms with annual sales less than $30 million.

**Fraction of sales based on total sales.

The census data show that nearly 99 percent of all the firms in the food sales,

foodservice, and lodging business sectors fall under the SBA definition of small businesses. 

Therefore, it can be concluded that there are a substantial number of firms that may be 

disproportionately disadvantaged by the new or amended standards.

In general, the subgroups that face higher cost of capital and below-average electricity 

prices are more disadvantaged than others. Higher cost of capital imposes burden on the

businesses because they have to borrow more money to purchase equipment that meets new or 

amended standards, compared to the case where there are no new or amended standards. Lower 

electricity price rates result in lower savings in energy costs and, consequently, lower LCC 

savings and higher PBPs.

Discount rates and average electricity price rates associated with different types of 

businesses are presented in TSD chapter 8. Of the three business types, lodging has the highest 

estimated discount rate at 6.76 percent (compared to 5.85 percent for foodservice and 5.14

percent for food sales). Based on Commercial Buildings Energy Consumption Survey (CBECS) 

data,6 foodservice faces the highest electricity prices at 104 percent of the average price for all 

commercial buildings, while lodging is at 97 percent and food sales is the lowest at 96 percent.

Lodging faces a cost of capital that is considerably higher than the other two categories, 

and was selected for a subgroup analysis. Foodservice faces a higher cost of capital than food

sales and the highest electricity prices. Foodservice was also selected for subgroup analysis. The 

11-6
higher cost of capital implies first cost is more important to this customer group than food sales 

customers, and also that they would value future savings less relative to the first cost.

DOE carried out two LCC subgroup analyses using the LCC spreadsheet described in 

TSD chapter 8, but with certain modifications. The input for business type was fixed to the 

identified subgroup, which ensured that the discount rates and electricity price rates associated 

with only that subgroup were selected in the Monte Carlo simulations (see TSD chapter 8). In the 

calculation of discount rates, it was assumed that 100 percent of customers face the small 

business interest rate premium when calculating discount rates. Apart from these changes, all 

other inputs for LCC subgroup analysis are the same as those in the LCC analysis described in 

TSD chapter 8. 

11.3 LIFE-CYCLE COST AND PAYBACK PERIOD RESULTS FOR SMALL 

BUSINESS SUBGROUPS

Table 11.3.1 presents the comparison of mean LCC savings for the small business 

subgroup in the foodservice sector with the national average values (LCC savings results from 

chapter 8 of the final rule TSD). For all trial standard levels (TSLs), the LCC savings for the 

small business subgroup show mixed results, with some higher and some lower than the national 

average values. Table 11.3.2 presents the percentage change in LCC savings compared to 

national average values. For TSLs 1–4 the percentage differences range from -3 to 3 percent for 

most equipment classes. The exceptions include IMH-A-Large-B2 where the TSLs 1 and 2 

results are 7 percent above the average, SCU-W-Large-B where the foodservice subgroup shows 

-4 percent savings compared to the average, and SCU-A-Large-B where the difference reaches -9 

percent relative to the average. DOE modeled all equipment classes in this analysis, although 

DOE believes it is likely that the very large equipment classes are not commonly used in 

foodservice establishments. 

11-7
Table 11.3.1 Comparison of Mean LCC Savings for the Small Business Subgroup in the 

Foodservice Sector with the National Average Values

Equipment Class Category

Mean LCC Savings 

2013$*

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH-W-Small-B Small Business 174 174 212 212 (535)

All Business Types 175 175 214 214 (534)

IMH-W-Med-B Small Business 312 312 312 168 (60)

All Business Types 308 308 308 165 (63)

IMH-W-Large-B Small Business NA NA NA NA (169)

All Business Types NA NA NA NA (172)

 IMH-W-Large-B1 Small Business NA NA NA NA (198)

All Business Types NA NA NA NA (200)

 IMH-W-Large-B2 Small Business NA NA NA NA (77)

All Business Types NA NA NA NA (80)

IMH-A-Small-B Small Business 139 75 78 78 (390)

All Business Types 136 72 77 77 (393)

IMH-A-Large-B Small Business 387 498 359 264 54 

All Business Types 382 501 361 265 55 

 IMH-A-Large-B1 Small Business 444 575 404 292 43 

All Business Types 439 580 407 294 45 

 IMH-A-Large-B2 Small Business 81 81 114 114 114 

All Business Types 76 76 110 110 110 

RCU-Large-B Small Business 754 754 754 424 150 

All Business Types 748 748 748 418 144 

 RCU-Large-B1 Small Business 749 749 749 397 166 

All Business Types 743 743 743 391 161 

 RCU-Large-B2 Small Business 832 832 832 832 (99)

All Business Types 820 820 820 820 (109)

SCU-W-Large-B Small Business 431 601 541 184 184 

All Business Types 444 613 550 192 192 

SCU-A-Small-B Small Business 112 162 276 226 (148)

All Business Types 110 161 281 230 (145)

SCU-A-Large-B Small Business 164 392 432 65 65 

All Business Types 163 400 439 71 71 

IMH-A-Small-C Small Business 248 296 317 317 (155)

All Business Types 245 292 313 313 (165)

IMH-A-Large-C Small Business 544 544 630 630 44 

All Business Types 539 539 626 626 28 

RCU-Small-C Small Business 503 453 509 509 (57)

All Business Types 498 448 505 505 (73)

SCU-A-Small-C Small Business 225 281 293 293 (257)

All Business Types 224 278 290 290 (268)

* Values in parentheses are negative numbers.

11-8
Table 11.3.2 Percentage Change in Mean LCC Savings for the Small Business Subgroup in 

the Foodservice Sector Compared to National Average Values*

Equipment Class TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH-W-Small-B (1%) (1%) (1%) (1%) 0%

IMH-W-Med-B 1% 1% 1% 2% 5%

IMH-W-Large-B NA NA NA NA 1%

 IMH-W-Large-B1 NA NA NA NA 1%

 IMH-W-Large-B2 NA NA NA NA 4%

IMH-A-Small-B 2% 3% 2% 2% 1%

IMH-A-Large-B 1% (1%) (1%) (1%) (2%)

 IMH-A-Large-B1 1% (1%) (1%) (1%) (4%)

 IMH-A-Large-B2 7% 7% 3% 3% 3%

RCU-Large-B 1% 1% 1% 1% 4%

 RCU-Large-B1 1% 1% 1% 1% 3%

 RCU-Large-B2 1% 1% 1% 1% 9%

SCU-W-Large-B (3%) (2%) (2%) (4%) (4%)

SCU-A-Small-B 1% 1% (2%) (2%) (2%)

SCU-A-Large-B 1% (2%) (2%) (9%) (9%)

IMH-A-Small-C 1% 1% 1% 1% 6%

IMH-A-Large-C 1% 1% 1% 1% 57%

RCU-Small-C 1% 1% 1% 1% 22%

SCU-A-Small-C 1% 1% 1% 1% 4%

* Values in parenthesis are negative numbers. Negative percentage values imply a decrease in 

LCC savings, and positive percentage values imply an increase in LCC savings.

Table 11.3.3 presents the comparison of median PBPs for the small business subgroup in 

the foodservice sector with national median values (median PBPs from chapter 8 of the final rule

TSD). The PBP values are shorter for the small business subgroup in all cases. This arises 

because the first-year operating cost savings—which are used for payback period—are higher,

leading to a shorter payback. (Given their higher discount rates, the foodservice customers value 

future savings less than the current capital expenditure, leading to the result of some cases with

lower LCC savings.) First-year savings are higher because the foodservice electricity prices are 

higher than the average of all classes.

Table 11.3.4 presents the comparison of mean LCC savings for the small business 

subgroup in lodging sector (hotels and casinos) with the national average values (LCC savings 

results from chapter 8 of the final rule TSD). Table 11.3.5 presents the percentage change in 

LCC savings of the lodging sector customer subgroup to national average values. For lodging 

sector small business, LCC savings are lower across the board. For 11 equipment classes, TSL 1 

through 3 savings range from 9 to 13 percent lower than the average. (For the IMH-W-Large-B 

class, TSLs 1–4 are not defined because the only efficiency level studies, the max tech level, was 

not cost effective to customers.) The IMH-A-Small-B class is the exception with the difference 

exceeding 20 percent lower than the average at TSLs 2 and 3. The differences are even greater at 

TSLs 4 and 5. The LCC savings are lower for lodging customers because the energy price for 

lodging is slightly lower than the average of all commercial business types (97 percent of the 

average for all commercial business types) while the lodging customers have higher discount 

rates than the average. The lower electricity price reduces the nominal value of future operating 

and maintenance benefits while the higher discount rate reduces the present value of the benefits, 

thus decreasing the LCC savings.

11-9
Table 11.3.3 Comparison of Median Payback Periods for the Small Business Subgroup in 

the Foodservice Sector with National Median Values

Equipment Class Category

Median Payback Period 

years

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH-W-Small-B Small Business 2.3 2.3 2.7 2.7 12.7 

All Business Types 2.5 2.5 2.7 2.7 13.4 

IMH-W-Med-B Small Business 2.0 2.0 2.0 4.8 7.2 

All Business Types 2.1 2.1 2.1 5.0 7.6 

IMH-W-Large-B Small Business NA NA NA NA 10.0 

All Business Types NA NA NA NA 10.6 

 IMH-W-Large-B1 Small Business NA NA NA NA 10.5 

All Business Types NA NA NA NA 11.1 

 IMH-W-Large-B2 Small Business NA NA NA NA 8.4 

All Business Types NA NA NA NA 8.9 

IMH-A-Small-B Small Business 3.2 4.5 4.4 4.4 11.4 

All Business Types 3.4 4.8 4.7 4.7 11.9 

IMH-A-Large-B Small Business 2.1 2.3 2.2 3.7 5.3 

All Business Types 2.2 2.4 2.3 3.9 5.6 

 IMH-A-Large-B1 Small Business 1.1 1.4 1.4 3.2 5.1 

All Business Types 1.2 1.5 1.5 3.4 5.4 

 IMH-A-Large-B2 Small Business 7.0 7.0 6.5 6.5 6.5 

All Business Types 7.4 7.4 6.9 6.9 6.9 

RCU-Large-B Small Business 1.0 1.0 1.0 3.2 4.8 

All Business Types 1.1 1.1 1.1 3.3 5.0 

 RCU-Large- B1 Small Business 0.9 0.9 0.9 3.2 4.7 

All Business Types 0.9 0.9 0.9 3.4 4.9 

 RCU-Large-B2 Small Business 2.8 2.8 2.8 2.8 6.7 

All Business Types 3.0 3.0 3.0 3.0 7.0 

SCU-W-Large-B Small Business 1.1 1.5 1.7 4.9 4.9 

All Business Types 1.1 1.6 1.8 5.1 5.1 

SCU-A-Small-B Small Business 2.0 2.2 2.5 3.3 8.4 

All Business Types 2.2 2.4 2.6 3.5 8.9 

SCU-A-Large-B Small Business 1.7 1.6 2.0 6.2 6.2 

All Business Types 1.8 1.6 2.1 6.5 6.5 

IMH-A-Small-C Small Business 1.4 1.5 1.6 1.6 8.3 

All Business Types 1.5 1.6 1.7 1.7 8.8 

IMH-A-Large-C Small Business 0.6 0.6 0.7 0.7 5.5 

All Business Types 0.7 0.7 0.7 0.7 5.9 

RCU-Small-C Small Business 0.7 1.1 1.2 1.2 5.5 

All Business Types 0.7 1.2 1.2 1.2 5.8 

SCU-A-Small-C Small Business 0.7 1.0 1.4 1.4 10.6 

All Business Types 0.8 1.1 1.5 1.5 11.4 

11-10
Table 11.3.4 Comparison of LCC Savings for the Small Business Subgroup in the Lodging

Sector with the National Average Values

Equipment Class Category

Mean LCC Savings 

2013$*

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH-W-Small-B Small Business 155 155 189 189 (561)

All Business Types 175 175 214 214 (534)

IMH-W-Med-B Small Business 275 275 275 123 (109)

All Business Types 308 308 308 165 (63)

IMH-W-Large-B Small Business NA NA NA NA (221)

All Business Types NA NA NA NA (172)

 IMH-W-Large-B1 Small Business NA NA NA NA (244)

All Business Types NA NA NA NA (200)

 IMH-W-Large-B2 Small Business NA NA NA NA (148)

All Business Types NA NA NA NA (80)

IMH-A-Small-B Small Business 118 54 61 61 (423)

All Business Types 136 72 77 77 (393)

IMH-A-Large-B Small Business 337 443 321 211 (10)

All Business Types 382 501 361 265 55 

 IMH-A-Large-B1 Small Business 398 523 368 237 (25)

All Business Types 439 580 407 294 45 

 IMH-A-Large-B2 Small Business 9 9 70 70 70 

All Business Types 76 76 110 110 110 

RCU-Large-B Small Business 679 679 679 347 71 

All Business Types 748 748 748 418 144 

 RCU-Large-B1 Small Business 676 676 676 322 90 

All Business Types 743 743 743 391 161 

 RCU-Large-B2 Small Business 718 718 718 718 (205)

All Business Types 820 820 820 820 (109)

SCU-W-Large-B Small Business 404 553 494 129 129 

All Business Types 444 613 550 192 192 

SCU-A-Small-B Small Business 98 142 248 196 (182)

All Business Types 110 161 281 230 (145)

SCU-A-Large-B Small Business 146 361 392 18 18 

All Business Types 163 400 439 71 71 

IMH-A-Small-C Small Business 222 263 282 282 (189)

All Business Types 245 292 313 313 (165)

IMH-A-Large-C Small Business 493 493 571 571 (33)

All Business Types 539 539 626 626 28 

RCU-Small-C Small Business 456 406 456 456 (133)

All Business Types 498 448 505 505 (73)

SCU-A-Small-C Small Business 204 253 261 261 (288)

All Business Types 224 278 290 290 (268)

* Values in parentheses are negative numbers.

11-11
Table 11.3.5 Percentage Change in the Mean LCC Savings for the Small Business 

Subgroup in the Lodging Sector Compared to the National Average Values

Equipment Class TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH-W-Small-B (11%) (11%) (12%) (12%) (5%)

IMH-W-Med-B (11%) (11%) (11%) (26%) (72%)

IMH-W-Large-B NA NA NA NA (29%)

 IMH-W-Large-B1 NA NA NA NA (22%)

 IMH-W-Large-B2 NA NA NA NA (84%)

IMH-A-Small-B (13%) (25%) (21%) (21%) (7%)

IMH-A-Large-B (12%) (12%) (11%) (20%) (118%)

 IMH-A-Large-B1 (9%) (10%) (10%) (19%) (155%)

 IMH-A-Large-B2 (88%) (88%) (37%) (37%) (37%)

RCU-Large-B (9%) (9%) (9%) (17%) (50%)

 RCU-Large-B1 (9%) (9%) (9%) (18%) (44%)

 RCU-Large-B2 (12%) (12%) (12%) (12%) (88%)

SCU-W-Large-B (9%) (10%) (10%) (33%) (33%)

SCU-A-Small-B (11%) (11%) (12%) (15%) (26%)

SCU-A-Large-B (10%) (10%) (11%) (75%) (75%)

IMH-A-Small-C (9%) (10%) (10%) (10%) (15%)

IMH-A-Large-C (9%) (9%) (9%) (9%) (215%)

RCU-Small-C (8%) (9%) (10%) (10%) (83%)

SCU-A-Small-C (9%) (9%) (10%) (10%) (7%)

* Values in parentheses are negative numbers. Negative percentage values imply 

a decrease in LCC savings, and positive percentage values imply an increase in 

LCC savings.

Table 11.3.6 presents the comparison of median PBPs for small business subgroup in the 

lodging sector with national median values (median PBPs from chapter 8 of the final rule TSD). 

The PBP values are slightly higher in the small business subgroup in all instances. As noted 

above, the energy savings should be lower in nominal terms than a national average.

11-12
Table 11.3.6 Comparison of Median Payback Periods for the Small Business Subgroup in 

the Lodging Sector with the National Median Values

Equipment Class Category

Median Payback Period 

years

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH-W-Small-B Small Business 2.5 2.5 2.8 2.8 13.5 

All Business Types 2.5 2.5 2.7 2.7 13.4 

IMH-W-Med-B Small Business 2.1 2.1 2.1 5.1 7.7 

All Business Types 2.1 2.1 2.1 5.0 7.6 

IMH-W-Large-B Small Business NA NA NA NA 10.7 

All Business Types NA NA NA NA 10.6 

 IMH-W-Large-B1 Small Business NA NA NA NA 11.2 

All Business Types NA NA NA NA 11.1 

 IMH-W-Large-B2 Small Business NA NA NA NA 9.0 

All Business Types NA NA NA NA 8.9 

IMH-A-Small-B Small Business 3.4 4.8 4.7 4.7 12.3 

All Business Types 3.4 4.8 4.7 4.7 11.9 

IMH-A-Large-B Small Business 2.2 2.4 2.3 3.9 5.7 

All Business Types 2.2 2.4 2.3 3.9 5.6 

 IMH-A-Large-B1 Small Business 1.2 1.5 1.5 3.4 5.4 

All Business Types 1.2 1.5 1.5 3.4 5.4 

 IMH-A-Large-B2 Small Business 7.5 7.5 6.9 6.9 6.9 

All Business Types 7.4 7.4 6.9 6.9 6.9 

RCU-Large-B Small Business 1.1 1.1 1.1 3.4 5.1 

All Business Types 1.1 1.1 1.1 3.3 5.0 

 RCU-Large-B1 Small Business 0.9 0.9 0.9 3.5 5.0 

All Business Types 0.9 0.9 0.9 3.4 4.9 

 RCU-Large-B2 Small Business 3.0 3.0 3.0 3.0 7.1 

All Business Types 3.0 3.0 3.0 3.0 7.0 

SCU-W-Large-B Small Business 1.1 1.6 1.8 5.2 5.2 

All Business Types 1.1 1.6 1.8 5.1 5.1 

SCU-A-Small-B Small Business 2.2 2.4 2.6 3.5 8.9 

All Business Types 2.2 2.4 2.6 3.5 8.9 

SCU-A-Large-B Small Business 1.8 1.6 2.1 6.6 6.6 

All Business Types 1.8 1.6 2.1 6.5 6.5 

IMH-A-Small-C Small Business 1.5 1.6 1.7 1.7 9.0 

All Business Types 1.5 1.6 1.7 1.7 8.8 

IMH-A-Large-C Small Business 0.7 0.7 0.7 0.7 6.0 

All Business Types 0.7 0.7 0.7 0.7 5.9 

RCU-Small-C Small Business 0.7 1.2 1.2 1.2 5.9 

All Business Types 0.7 1.2 1.2 1.2 5.8 

SCU-A-Small-C Small Business 0.8 1.1 1.5 1.5 11.7 

All Business Types 0.8 1.1 1.5 1.5 11.4 

The LCC subgroup analysis results show that the identified foodservice subgroup has 

mixed results with some equipment classes showing higher than average LCC savings while 

others are lower, but for TSLs 1–4 the differences are mostly small. The lodging sector

customers would all receive lower LCC savings due to their higher cost of capital and lower 

electricity prices. Foodservice has a lower PBP than average because of higher-than-average

electricity prices. Payback periods for lodging business sector customers are higher than the 

average.

11-13
REFERENCES

1. U.S. Department of Education, National Center for Education Statistics. Table 90. Number 

of public school districts and public and private elementary and secondary schools: 

Selected years, 1869-70 through 2008-09. (Last accessed January 3, 2012.) 

<http://nces.ed.gov/programs/digest/d10/tables/dt10_090.asp>

2. American Hospital Association. Fast Facts on Hospitals. 2012. Chicago, IL, January 3, 

2012. (Last accessed January 3, 2012.) <www.aha.org/> 

3. Title 13, Code of Federal Regulations, Chapter I–Small Business Administration, Part 

121–Small Business Administration, Subpart A–Size Eligibility Provisions and Standards.

May 4, 2005.

4. U.S. Census Bureau. 2007 Economic Census: Retail Trade: Subject Series–Estab & Firm 

Size: Summary Statistics for Single Unit and Multiunit Firms for the United States: 2007. 

(Last accessed October 30, 2013.) 

<http://factfinder2.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=ECN_

2007_US_44SSSZ3&prodType=table>

5 U.S. Census Bureau. 2007 Economic Census: Accommodation and Food Services: Subject 

Series - Estab and Firm Size: Summary Statistics by Sales Size of Firms for the United 

States: 200. (Last accessed October 30, 2013.) 

<http://factfinder2.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=ECN_

2007_US_72SSSZ4&prodType=table>

6. U.S. Department of Energy—Energy Information Agency, Commercial Buildings Energy 

Consumption Survey (CBECS). CBECS Table C-14A Electricity Consumption and 

Expenditure Intensities for All Buildings (Including Malls). 2003. ( Last accessed March 8, 

2011.) <www.eia.gov/consumption/commercial/index.cfm> 

11-14
CHAPTER 12. MANUFACTURER IMPACT ANALYSIS

TABLE OF CONTENTS

12.1 INTRODUCTION .........................................................................................................12-1

12.2 METHODOLOGY ........................................................................................................12-1

12.2.1 Phase I: Industry Profile ..................................................................................12-2

12.2.2 Phase II: Industry Cash-Flow Model and Interview Guide.............................12-2

12.2.2.1 Industry Cash-Flow Analysis............................................................12-2

12.2.2.2 Interview Guide .................................................................................12-3

12.2.3 Phase III: Subgroup Analysis..........................................................................12-3

12.2.3.1 Manufacturing Interviews .................................................................12-3

12.2.3.2 Revised Industry Cash-Flow Analysis ..............................................12-3

12.2.3.3 Manufacturer Subgroup Analysis......................................................12-4

12.2.3.4 Small-Business Manufacturers..........................................................12-4

12.2.3.5 Manufacturing Capacity Impact ........................................................12-5

12.2.3.6 Employment Impact ..........................................................................12-5

12.2.3.7 Cumulative Regulatory Burden.........................................................12-5

12.3 GRIM INPUTS AND ASSUMPTIONS........................................................................12-5

12.3.1 Overview of the GRIM....................................................................................12-5

12.3.2 Sources for GRIM Inputs ................................................................................12-6

12.3.2.1 Corporate Annual Reports.................................................................12-6

12.3.2.2 Standard and Poor Credit Ratings .....................................................12-7

12.3.2.3 Shipment Model ................................................................................12-7

12.3.2.4 Engineering Analysis.........................................................................12-7

12.3.2.5 Manufacturer Interviews ...................................................................12-7

12.3.3 Financial Parameters .......................................................................................12-8

12.3.4 Corporate Discount Rate .................................................................................12-8

12.3.5 Trial Standard Levels ....................................................................................12-10

12.3.6 NIA Shipment Forecast .................................................................................12-12

12.3.7 Production Costs............................................................................................12-12

12.3.8 Conversion Costs...........................................................................................12-21

12.3.8.1 Automatic Commercial Ice Maker Product and Capital Conversion 

Costs..............................................................................................................12-21

12.3.9 Markup Scenarios..........................................................................................12-22

12.3.9.1 Preservation of Gross Margin Markup Scenario .............................12-22

12.3.9.2 Preservation of EBIT Markup Scenario ..........................................12-22

12.4 INDUSTRY FINANCIAL IMPACTS ........................................................................12-32

12.4.1 Impacts on Industry Net Present Value .........................................................12-33

12.4.2 Impacts on Annual Cash Flow ......................................................................12-34

12.5 IMPACTS ON SMALL AUTOMATIC COMMERCIAL ICE MAKER 

MANUFACTURERS ..................................................................................................12-35

12.5.1 Description and Estimated Number of Small Entities Regulated .................12-36

12.5.2 Description and Estimate of Compliance Requirements...............................12-37

12.6 OTHER IMPACTS......................................................................................................12-38

12-i
12.6.1 Employment ..................................................................................................12-38

12.6.1.1 Impacts on Direct Employment.......................................................12-38

12.6.2 Production Capacity ......................................................................................12-40

12.6.3 Cumulative Regulatory Burden.....................................................................12-41

12.6.3.1 Other Regulations That Could Impact Automatic Commercial Ice 

Maker Manufacturers ....................................................................................12-41

12.7 CONCLUSION............................................................................................................12-42

12.7.1 Automatic Commercial Ice Makers...............................................................12-42

REFERENCES ........................................................................................................................12-45

LIST OF TABLES

Table 12.2.1 SBA and NAICS Classification of Small Businesses Potentially Affected by This 

Rulemaking....................................................................................................................12-4

Table 12.3.1 GRIM Financial Parameters based on 2005–2011 Weighted Company Financial 

Data ................................................................................................................................12-8

Table 12.3.2 Cost of Equity Calculation....................................................................................12-9

Table 12.3.3 Cost of Debt Calculation.....................................................................................12-10

Table 12.3.4 Crosswalk between Equipment Class, Capacity Group, and Capacity Group 

Number ........................................................................................................................12-11

Table 12.3.5 Manufacturer Production Cost Breakdown (2013$) for IMH-W-Small-B ........12-13

Table 12.3.6 Manufacturer Production Cost Breakdown (2013$) for IMH-W-Medium-B ..12-13

Table 12.3.7 Manufacturer Production Cost Breakdown (2013$) for IMH-W-Large-B ........12-13

Table 12.3.8 Manufacturer Production Cost Breakdown (2013$) for IMH-W-Ext1-B..........12-13

Table 12.3.9 Manufacturer Production Cost Breakdown (2013$) for IMH-W-Ext2-B..........12-13

Table 12.3.10 Manufacturer Production Cost Breakdown (2013$) for IMH-A-Small-B .......12-13

Table 12.3.11 Manufacturer Production Cost Breakdown (2013$) for IMH-A-Medium-B...12-14

Table 12.3.12 Manufacturer Production Cost Breakdown (2013$) for IMH-A-Large-B .......12-14

Table 12.3.13 Manufacturer Production Cost Breakdown (2013$) for IMH-A-Ext-B...........12-14

Table 12.3.14 Manufacturer Production Cost Breakdown (2013$) for RCU-NRC-Small-B..12-14

Table 12.3.15 Manufacturer Production Cost Breakdown (2013$) for RCU-NRC-Large-B..12-14

Table 12.3.16 Manufacturer Production Cost Breakdown (2013$) for RCU-NRC-Ext1-B ...12-15

Table 12.3.17 Manufacturer Production Cost Breakdown (2013$) for RCU-NRC-Ext1-B2 .12-15

Table 12.3.18 Manufacturer Production Cost Breakdown (2013$) for RCU-RC-Small-B ....12-15

Table 12.3.19 Manufacturer Production Cost Breakdown (2013$) for RCU-RC-Large-B ....12-15

Table 12.3.20 Manufacturer Production Cost Breakdown (2013$) for RCU-RC-Ext1-B......12-15

Table 12.3.21 Manufacturer Production Cost Breakdown (2013$) for RCU-RC-Ext2-B.....12-15

Table 12.3.22 Manufacturer Production Cost Breakdown (2013$) for SCU-W-Small-B ......12-16

Table 12.3.23 Manufacturer Production Cost Breakdown (2013$) for SCU-W-Large-B ......12-16

Table 12.3.24 Manufacturer Production Cost Breakdown (2013$) for SCU-A-Small-B .......12-16

Table 12.3.25 Manufacturer Production Cost Breakdown (2013$) for SCU-A-Large-B .......12-16

Table 12.3.26 Manufacturer Production Cost Breakdown (2013$) for SCU-A-Ext-B...........12-17

Table 12.3.27 Manufacturer Production Cost Breakdown (2013$) for IMH-W-Small-C ......12-17

Table 12.3.28 Manufacturer Production Cost Breakdown (2013$) for IMH-W-Large-C ......12-17

Table 12.3.29 Manufacturer Production Cost Breakdown (2013$) for IMH-A-Small-C .......12-17

Table 12.3.30 Manufacturer Production Cost Breakdown (2013$) for IMH-A-Large-C .......12-18

12-ii
Table 12.3.31 Manufacturer Production Cost Breakdown (2013$) for IMH-A-Ext-C...........12-18

Table 12.3.32 Manufacturer Production Cost Breakdown (2013$) for RCU-NRC-Small-C..12-18

Table 12.3.33 Manufacturer Production Cost Breakdown (2013$) for RCU-NRC-Large-C..12-19

Table 12.3.34 Manufacturer Production Cost Breakdown (2013$) for RCU-RC-Small-C ....12-19

Table 12.3.35 Manufacturer Production Cost Breakdown (2013$) for RCU-RC-Large-C ....12-19

Table 12.3.36 Manufacturer Production Cost Breakdown (2013$) for SCU-W-Small-C ......12-19

Table 12.3.37 Manufacturer Production Cost Breakdown (2013$) for SCU-W-Large-C ......12-20

Table 12.3.38 Manufacturer Production Cost Breakdown (2013$) for SCU-A-Small-C .......12-20

Table 12.3.39 Manufacturer Production Cost Breakdown (2013$) for SCU-A-Large-C .......12-20

Table 12.3.40 Manufacturer Production Cost Breakdown (2013$) for SCU-A-Ext-C...........12-20

Table 12.3.41 Product and Capital Conversion Costs for Standard Ice Makers by TSL ........12-21

Table 12.3.42 Preservation of Gross Margin Markups for Automatic Commercial Ice Makers, 

Representative of all Equipment Classes.....................................................................12-22

Table 12.3.43 Preservation of EBIT Markups for IMH-W-Small-B.......................................12-23

Table 12.3.44 Preservation of EBIT Markups for IMH-W-Medium-B..................................12-23

Table 12.3.45 Preservation of EBIT for IMH-W-Large-B......................................................12-23

Table 12.3.46 Preservation of EBIT Markups for IMH-W-Ext1-B.........................................12-23

Table 12.3.47 Preservation of EBIT Markups for IMH-W-Ext2-B.........................................12-23

Table 12.3.48 Preservation of EBIT Markups for IMH-A-Small-B........................................12-24

Table 12.3.49 Preservation of EBIT Markups for IMH-A-Medium-B ...................................12-24

Table 12.3.50 Preservation of EBIT Markups for IMH-A-Large-B........................................12-24

Table 12.3.51 Preservation of EBIT Markups for IMH-A-Ext-B ...........................................12-24

Table 12.3.52 Preservation of EBIT Markups for RCU-NRC-Small-B..................................12-25

Table 12.3.53 Preservation of EBIT Markups for RCU-NRC-Large-B..................................12-25

Table 12.3.54 Preservation of EBIT Markups for RCU-NRC-Ext1-B....................................12-25

Table 12.3.55 Preservation of EBIT Markups for RCU-NRC-Ext1-B2..................................12-25

Table 12.3.56 Preservation of EBIT Markups for RCU-RC-Small-B.....................................12-25

Table 12.3.57 Preservation of EBIT Markups for RCU-RC-Large-B.....................................12-26

Table 12.3.58 Preservation of EBIT Markups for RCU-RC-Ext1-B ......................................12-26

Table 12.3.59 Preservation of EBIT Markups for RCU-RC-Ext2-B ......................................12-26

Table 12.3.60 Preservation of EBIT Markups for SCU-W-Small-B.......................................12-26

Table 12.3.61 Preservation of EBIT Markups for SCU-W-Large-B.......................................12-27

Table 12.3.62 Preservation of EBIT Markups for SCU-A-Small-B........................................12-27

Table 12.3.63 Preservation of EBIT Markups for SCU-A-Large-B........................................12-27

Table 12.3.64 Preservation of EBIT Markups for SCU-A-Ext-B ...........................................12-28

Table 12.3.65 Preservation of EBIT Markups for IMH-W-Small-C.......................................12-28

Table 12.3.66 Preservation of EBIT Markups for IMH-W-Large-C.......................................12-28

Table 12.3.67 Preservation of EBIT Markups for IMH-A-Small-C........................................12-29

Table 12.3.68 Preservation of EBIT Markups for IMH-A-Large-C........................................12-29

Table 12.3.69 Preservation of EBIT Markups for IMH-A-Ext-C ...........................................12-29

Table 12.3.70 Preservation of EBIT Markups for RCU-NRC-Small-C..................................12-30

Table 12.3.71 Preservation of EBIT Markups for RCU-NRC-Large-C..................................12-30

Table 12.3.72 Preservation of EBIT Markups for RCU-RC-Small-C.....................................12-30

Table 12.3.73 Preservation of EBIT Markups for RCU-RC-Large-C.....................................12-31

Table 12.3.74 Preservation of EBIT Markups for SCU-W-Small-C.......................................12-31

Table 12.3.75 Preservation of EBIT Markups for SCU-W-Large-C.......................................12-31

12-iii
Table 12.3.76 Preservation of EBIT Markups for SCU-A-Small-C........................................12-32

Table 12.3.77 Preservation of EBIT Markups for SCU-A-Large-C........................................12-32

Table 12.3.78 Preservation of EBIT Markups for SCU-A-Ext-C ...........................................12-32

Table 12.4.1 Manufacturer Impact Analysis for Automatic Commercial Ice Makers –

Preservation of Gross Margin Markup Scenario .........................................................12-33

Table 12.4.2 Manufacturer Impact Analysis for Automatic Commercial Ice Makers –

Preservation of EBIT Markup Scenario.......................................................................12-34

Table 12.5.1 Comparison of Small Business Manufacturers of Automatic Commercial Ice Maker 

INPV to that of the Industry at Large by TSL under the Preservation of Gross Margin 

Markup Scenario*........................................................................................................12-38

Table 12.5.2 Comparison of Small Business Manufacturers of Automatic Commercial Ice Maker 

INPV to that of the Industry at Large by TSL under the Preservation of EBIT Markup 

Scenario........................................................................................................................12-38

Table 12.6.1 Potential Changes in the Total Number of Domestic Automatic Commercial Ice 

Maker Production Workers in 2018*...........................................................................12-40

Table 12.6.2 Compliance Dates and Expected Conversion Expenses of Federal Energy 

Conservation Standards Affecting Automatic Commercial Ice Maker Manufacturers...12-

41

LIST OF FIGURES

Figure 12.3.1 Using the GRIM to Calculate Cash Flow............................................................12-6

Figure 12.4.1 Annual Industry Net Cash Flows for Automatic Commercial Ice Makers 

(Preservation of Gross Margin Markup Scenario).......................................................12-35

Figure 12.4.2 Annual Industry Net Cash Flows for Automatic Commercial Ice Makers 

(Preservation of EBIT Markup Scenario)....................................................................12-35

12-iv
12.0 MANUFACTURER IMPACT ANALYSIS

12.1 INTRODUCTION

In determining whether a standard is economically justified, the U.S. Department of 

Energy (DOE) is required to consider “the economic impact of the standard on the manufacturers 

and on the consumers of the products subject to such a standard.” (42 U.S.C. 6295(o)(2)(B)(i)) 

The statute also calls for an assessment of the impact of any lessening of competition as 

determined in writing by the Attorney General. Id. DOE conducted a manufacturer impact 

analysis (MIA) to estimate the financial impact of more stringent energy conservation standards 

on manufacturers of automatic commercial ice makers, and assessed the impact of such standards 

on direct employment and manufacturing capacity.

The MIA has both quantitative and qualitative aspects. The quantitative part of the MIA 

primarily relies on the Government Regulatory Impact Model (GRIM), an industry cash-flow 

model adapted for each equipment class in this rulemaking. The GRIM inputs include 

information on industry cost structure, shipments, and pricing strategies. The GRIM’s key output 

is the industry net present value (INPV), which is the sum of discounted industry cash-flow over 

the analysis period. The model estimates the financial impact of more stringent energy 

conservation standards by comparing changes in INPV between a base case and at each trial 

standard levels (TSLs) in the standards case. The qualitative part of the MIA addresses product 

characteristics, manufacturer characteristics, market and product trends, as well as the impact of 

standards on subgroups of manufacturers.

12.2 METHODOLOGY

DOE conducted the MIA in three phases. Phase I, “Industry Profile,” consisted of 

preliminary research directed at characterizing the automatic commercial ice maker

manufacturing industry. This research involved collecting data on market share, sales volumes 

and trends, pricing, employment, and the industry financial structure.

In Phase II, “Industry Cash Flow,” DOE created a framework discounted cash flow

model to analyze the economic impact of new and amended energy conservation standards on 

the automatic commercial ice maker manufacturing industry as a whole. DOE presented the MIA 

results for automatic commercial ice makers based on a set of considered TSLs. These TSLs are 

described in section 12.3.5.

In Phase III, “Subgroup Impact Analysis,” DOE interviewed manufacturers representing 

over 95 percent of the automatic commercial ice maker market. Interviewees included 

manufacturers with various market shares and product focus, providing a representative crosssection of the industry. During interviews, DOE discussed financial topics specific to each 

manufacturer and obtained each manufacturer’s view of the industry. The interviews provided 

DOE with valuable information for evaluating the impacts of new and amended energy 

conservation standards on manufacturer cash flows, investments, and employment.

12-1
12.2.1 Phase I: Industry Profile

In Phase I of the MIA, DOE prepared a profile of the automatic commercial ice maker

industry that built upon the market and technology assessment prepared for this rulemaking. (See 

chapter 3 of the Technical Support Document (TSD). Before initiating the detailed impact 

studies, DOE collected information on the present and past market structure and characteristics 

of the industry, tracking trends in market share, product attributes, product shipments, 

manufacturer markups, and the cost structure for various manufacturers.

The profile also included a top-down analysis of manufacturers in the industry using 

Security and Exchange Commission (SEC) 10–K filings,

a Standard & Poor (S&P) stock reports,b

and corporate annual reports released by both public and privately held companies. DOE used 

this and other publicly available information to derive preliminary financial inputs for the GRIM 

(e.g. revenues; cost of goods sold; depreciation; selling, general and administrative expenses 

(SG&A); and research and development (R&D) expenses).

12.2.2 Phase II: Industry Cash-Flow Model and Interview Guide

Phase II focused on the financial impacts of new and amended energy conservation 

standards on the automatic commercial ice maker manufacturing industry as a whole. New and 

amended energy conservation standards can affect manufacturer cash flows in three distinct 

ways: (1) by creating a need for increased investment, (2) by raising production costs per unit, 

and (3) by altering revenue due to higher per-unit prices and/or possible changes in sales 

volumes. DOE created a framework discounted cash-flow model to analyze the economic impact 

of new and amended energy conservation standards on the automatic commercial ice maker

manufacturing industry as a whole. In preparing this model, DOE used the financial values 

derived during Phase I and the shipment assumptions from the NIA. Additionally, DOE prepared 

a written guide for manufacturer interviews to collect additional data critical to developing other

inputs for the cash-flow analysis.

12.2.2.1 Industry Cash-Flow Analysis

The model uses several factors to determine a series of annual cash flows from the 

announcement year of new and amended energy conservation standards until several years after 

the standards’ compliance date. INPV is the sum of these annual cash flows discounted by the 

industry weighted average cost of capital. Inputs to the model include the manufacturing costs, 

markups, and shipment forecasts developed in other analyses as well as the industry weighted 

average financial parameters developed in Phase I. DOE derived the manufacturing costs from 

the engineering analysis as presented in chapter 5 of the TSD, information provided by the 

industry, publicly available financial reports, and interviews with manufacturers. To examine the 

range of possible impacts, DOE developed alternative markup scenarios based on discussions 

with manufacturers. DOE’s shipments analysis, presented in chapter 9 of the TSD, provided the 

basis for the shipment projections. DOE derived the financial parameters using publicly available 

reports and revised them using information submitted confidentially during manufacturer 

interviews. DOE used the GRIM to compare INPV in the base case with INPV at various TSLs 

a Available online at www.sec.gov. b Available online at www2.standardandpoors.com.

12-2

 
(the standards cases). The difference in INPV between the base and standards cases represents 

the financial impact of the new and amended standard on manufacturers.

12.2.2.2 Interview Guide

During Phase III of the MIA, DOE interviewed manufacturers of automatic commercial

ice makers to gather information on the effects of new and amended energy conservation on 

revenues and finances, direct employment, capital assets, and industry competitiveness. Before 

the interviews, DOE distributed an interview guide to representatives of each participating 

manufacturer. The interview guide provided a starting point to help identify relevant issues and 

understand the impacts of new and amended energy conservation standards on individual 

manufacturers or subgroups of manufacturers. Most of the information DOE received from these 

meetings is protected by non-disclosure agreements and resides with DOE’s contractors. The 

topics covered as part of these interviews include (1) key issues to this rulemaking; (2) 

engineering and life cycle cost follow-up; (3) manufacturer markups and profitability; (4) 

financial parameters; and (5) conversion costs. 

12.2.3 Phase III: Subgroup Analysis

While conducting the MIA, DOE interviewed a representative cross-section of automatic 

commercial ice maker manufacturers. The MIA interviews broadened the discussion to include 

business-related topics. DOE sought to obtain feedback from industry on the approaches used in 

the cash flow model and to isolate key issues and concerns. During interviews, DOE identified

one manufacturer subgroup, small businesses, that would warrant a subgroup analysis.

12.2.3.1 Manufacturing Interviews

The information gathered in Phase I and the cash-flow analysis performed in Phase II are 

supplemented with information gathered from manufacturer interviews in Phase III. The 

interview process provides an opportunity for interested parties to express their views on 

important issues privately, allowing confidential or sensitive information to be considered in the 

rulemaking process.

DOE used these interviews to tailor the cash flow model to reflect the unique financial 

characteristics of the automatic commercial ice maker manufacturing industry. Companies with 

various market shares and product focus were interviewed to provide a representation of the 

industry. Interviews were scheduled well in advance to provide every opportunity for key 

individuals to be available for comment. Although a written response to the questionnaire was 

acceptable, DOE sought interactive interviews, which help clarify responses and identify 

additional issues. The resulting information provides valuable inputs to the cash-flow model.

12.2.3.2 Revised Industry Cash-Flow Analysis

In Phase II of the MIA, DOE provided manufacturers with preliminary financial figures 

for review and evaluation. During the interviews, DOE requested comments on the values it 

selected for the parameters. DOE revised its industry cash-flow model based on this feedback. 

Section 12.3.3 provides more information on how DOE calculated the parameters.

12-3
12.2.3.3 Manufacturer Subgroup Analysis 

Using average cost assumptions to develop an industry cash flow estimate may not 

adequately assess differential impacts of new and amended energy conservation standards on 

manufacturer subgroups. For example, small businesses, manufacturers of niche products, or 

companies exhibiting a cost structure that differs significantly from the industry average could be 

more negatively affected. DOE used the results of the industry characterization to group 

manufacturers exhibiting similar characteristics. During the interviews, DOE discussed the 

potential subgroups and subgroup members it identified for the analysis. DOE asked 

manufacturers and other interested parties to suggest what subgroups or characteristics are the 

most appropriate to analyze. Through the course of the rulemaking, DOE identified small 

business manufacturers as a subgroup which requires separate analysis.

12.2.3.4 Small-Business Manufacturers

DOE used the Small Business Administration (SBA) small business size standards as 

new and amended by the Office of Management and Budget on January 1, 2012, and effective 

January 7, 2013, and the North American Industry Classification System (NAICS) code, 

presented in Table 12.2.1, to determine whether any small entities would be affected by the 

rulemaking.c For the equipment classes under review, the SBA bases its small business definition 

on the total number of employees for a business including the total employee count of a parent 

company and its subsidiaries. An aggregated business entity with fewer employees than the listed 

limit is considered a small business.

Table 12.2.1 SBA and NAICS Classification of Small Businesses Potentially Affected by 

This Rulemaking

Industry Description Revenue Limit Employee Limit NAICS

Air-Conditioning and Warm Air Heating Equipment 

and Commercial and Industrial Refrigeration 

Equipment Manufacturing

N/A 750 333415

DOE used the Air-Conditioning, Heating, and Refrigeration Institute (AHRI)

1 certified 

product directory as well as public certification databases California Energy Commission 

(CEC),2 and ENERGY STAR3 to identify manufacturers of automatic commercial ice makers. 

DOE also consulted publicly available data from the Small Business Association (SBA) to 

determine the presence of any additional small businesses in the industry. Further, DOE asked 

interested parties and industry representatives if they were aware of other small business 

manufacturers and checked any companies identified against the small business criteria. During 

its research, DOE identified seven manufacturers of automatic commercial ice makers that 

qualify as small businesses as specified by the SBA employee limits. DOE calculated separate 

INPV and cash flow numbers for small businesses in the automatic commercial ice makers 

GRIM. These figures can be found in the “Summary_SB” tab of the GRIM.

c The size standards are available on the SBA’s website at www.sba.gov/content/table-small-business-size-standards

12-4

 
12.2.3.5 Manufacturing Capacity Impact

One significant outcome of new and amended energy conservation standards could be the 

obsolescence of existing manufacturing assets, including tooling and production equipment. The 

manufacturer interview guide contains a series of questions to help identify impacts of new and 

amended standards on manufacturing capacity, specifically capacity utilization and plant location 

decisions in the United States and North America, with and without new and amended standards; 

the ability of manufacturers to upgrade or remodel existing facilities to accommodate the new 

requirements; the nature and value of any stranded assets; and estimates for any one-time 

changes to existing plant, property, and equipment (PPE). DOE’s estimates of the one-time 

capital changes and stranded assets affect the annual cash flow projections in the model. These 

estimates can be found in section 12.3.8; DOE’s discussion of the capacity impact can be found 

in section 12.6.2.

12.2.3.6 Employment Impact 

The impact of new and amended energy conservation standards on employment is an 

important consideration in the rulemaking process. To assess how domestic direct employment 

patterns might be affected, the interviews explored current employment trends in the Automatic 

Commercial Ice Maker industry. The interviews also solicited manufacturer views on changes in 

employment patterns that may result from more stringent standards. The employment impacts 

section of the interview guide focused on current employment levels associated with 

manufacturers at each production facility, expected future employment levels with and without 

new and amended energy conservation standards, and differences in workforce skills and issues 

related to the retraining of employees. The employment impacts are reported in section 12.6.1.

12.2.3.7 Cumulative Regulatory Burden

DOE seeks to mitigate the overlapping effects on manufacturers due to new and amended

energy conservation standards and other regulatory actions affecting the same products. DOE 

analyzed the impact on manufacturers of multiple, product-specific regulatory actions. Based on 

its own research and discussions with manufacturers, DOE identified regulations relevant to 

Automatic Commercial Ice Maker manufacturers, such as other Federal regulations that impact 

other products made by the same manufacturers. Discussion of the cumulative regulatory burden 

can be found in section 12.6.3.

12.3 GRIM INPUTS AND ASSUMPTIONS

The GRIM serves as the main tool for assessing the impacts on industry due to new and amended

energy conservation standards analyzed in this rulemaking. DOE relies on several sources to 

obtain inputs for the GRIM. Data and assumptions from these sources are then fed into an 

accounting model that calculates the industry cash flow both with and without new and amended

energy conservation standards.

12.3.1 Overview of the GRIM

The basic structure of the GRIM, illustrated in Figure 12.3.1, is an annual cash flow 

analysis that uses manufacturer prices, manufacturing costs, shipments, and industry financial 

12-5
information as inputs, and accepts a set of regulatory conditions such as changes in costs, 

investments, and associated margins. The GRIM spreadsheet uses a number of inputs to arrive at 

a series of annual cash flows, beginning with the base year of the analysis, 2015, and continuing 

to 2047. The model calculates the INPV by summing the stream of annual discounted cash flows 

during this period.

Figure 12.3.1 Using the GRIM to Calculate Cash Flow

The GRIM projects cash flows using standard accounting principles and compares 

changes in INPV between the base-case and the standard-case scenario induced by new and 

amended energy conservation standards. The difference in INPV between the base case and the 

standard case(s) represents the estimated financial impact of the new and amended energy 

conservation standards on manufacturers. Appendix 12-A provides more technical details and 

user information for the GRIM.

12.3.2 Sources for GRIM Inputs

The GRIM uses several different sources for data inputs in determining industry cash 

flow. These sources include corporate annual reports, company profiles, U.S. Census data, credit

ratings, the shipments model, the engineering analysis, and the manufacturer interviews.

12.3.2.1 Corporate Annual Reports

Corporate annual reports for publicly held companies are freely available to the general 

public through the SEC as filings of Form 10-K. Additionally, some privately held companies 

publish annual financial reports on their corporate websites. DOE developed initial financial 

inputs to the GRIM by examining the publicly available annual reports of companies primarily 

engaged in the manufacture of home appliances whose combined product range includes 

Automatic Commercial Ice Makers. As these companies do not provide detailed information 

about their individual product lines, DOE used the aggregate financial information at the 

corporate level in developing its initial estimates of the financial parameters to be used in the 

12-6
GRIM. In doing so, DOE assumes that the industry-average figures calculated for these 

companies were representative of manufacturing for Automatic Commercial Ice Makers. These 

figures were later revised using feedback from interviews to be representative of manufacturing 

for each product. DOE used corporate annual reports to derive the following initial inputs to the 

GRIM:

• Tax rate;

• Working capital;

• SG&A;

• R&D;

• Depreciation;

• Capital expenditures; and

• Net property, plant, and equipment.

12.3.2.2 Standard and Poor Credit Ratings

S&P provides independent credit ratings, research, and financial information. DOE relied 

on S&P reports to determine the industry’s average cost of debt when calculating the weightedaverage cost of capital.

12.3.2.3 Shipment Model

The GRIM used shipment projections from DOE’s shipments model in the national 

impact analysis (NIA). The model relied on historical shipments data for Automatic Commercial

Ice Makers. Chapter 9 of the TSD describes the methodology and analytical model DOE used to 

forecast shipments.

12.3.2.4 Engineering Analysis 

DOE conducted the engineering analyses for this rulemaking using the efficiency-level 

approach, combined with the cost-assessment approach, to develop a cost for each efficiency 

level in each equipment class and capacity grouping of Automatic Commercial Ice Makers. 

During this analysis, DOE used a manufacturing cost model to develop manufacturer production 

cost (MPC) estimates for Automatic Commercial Ice Makers. The analysis yielded the labor, 

materials, overhead, and total production costs for products at each efficiency level. The 

engineering analysis also estimated a manufacturer markup to determine the manufacturer selling 

price (MSP) for each product at every efficiency level. Chapter 5 of the TSD describes the 

engineering analysis in detail.

12.3.2.5 Manufacturer Interviews

As part of the MIA, DOE conducted interviews with a representative cross-section of 

manufacturers. Through these discussions, DOE obtained information to determine and verify 

GRIM input assumptions. Key topics discussed during the interviews and reflected in the GRIM 

include:

• Capital conversion costs (one-time investments in plant, property and equipment);

12-7
• Product conversion costs (one-time investments in research, product development, 

testing, and marketing);

• Production cost structure, or the portion of the MPCs related to materials, labor, 

overhead, and depreciation costs;

• Projected total shipment and shipment distribution mix; and

• MPCs estimated in the engineering analysis.

12.3.3 Financial Parameters

In the manufacturer interviews, DOE used the financial parameters from 2003 to 2010 for 

four appliance manufacturers with a combined market share of over 90 percent as a starting point

for determining the Automatic Commercial Ice Maker industry financial parameters. The 

industry financial parameters were determined by weighting each manufacturer’s individual 

financial parameters by their respective market share, and correcting for the fraction of the 

market that was not represented. Table 12.3.1 below shows the data used to determine the initial 

financial parameter estimates.

Table 12.3.1 GRIM Financial Parameters based on 2005–2011 Weighted Company 

Financial Data

Parameter

IndustryWeighted 

Average

Manufacturer

A B C D

Tax Rate (% of Taxable Income) 33.6 42.6 25.4 14.0 30.7

Working Capital (% of Revenue) 9.3 11.9 20.7 3.8 3.9

SG&A (% of Revenue) 15.4 17.8 24.3 13.1 10.4

R&D (% of Revenues) 1.5 1.8 2.8 2.8 2.4

Depreciation (% of Revenues) 2.1 2.8 3.4 2.2 3.1

Capital Expenditures (% of Revenues) 1.5 3.1 4.2 2.6 3.2

Net Property, Plant, and Equipment (% of Revenues) 16.4 14.4 16.3 20.9 17.6

During interviews, manufacturers were asked to provide their own figures for the 

parameters listed in Table 12.3.1. DOE adjusted the tax rate, depreciation and capital 

expenditures according to the manufacturers’ feedback.

12.3.4 Corporate Discount Rate

A company’s assets are financed by a combination of debt and equity, and the weightedaverage cost of capital (WACC) represents the minimum rate of return necessary to cover the 

debt and equity obligations manufacturers use to finance operations. The WACC is the total cost 

of debt and equity weighted by their respective proportions in the capital structure of the 

company. 

DOE estimated the WACC for Automatic Commercial Ice Maker industry based on 

several representative companies, using the following formula:

WACC = After-Tax Cost of Debt x (Debt Ratio) + Cost of Equity x (Equity Ratio) 

12-8
The cost of equity is the rate of return that equity investors (including, potentially, the 

company) expect to earn on a company’s stock. These expectations are reflected in the market 

price of the company’s stock. The capital asset pricing model (CAPM) provides one widely used 

means to estimate the cost of equity. According to the CAPM, the cost of equity (expected 

return) is:

Cost of Equity = Risk-free Rate of Return + β x Risk Premium 

where:

Risk-free rate of return is the rate of return on a “safe” benchmark investment, typically 

considered the short-term Treasury Bill (T-Bill) yield. In practice, investors use a variety of 

different maturity T-Bills to estimate the risk-free rate. DOE used the 10-year T-Bill return 

because it captures long-term inflation expectations and is less volatile than short-term rates. The 

risk-free rate is estimated to be approximately 5.2 percent, which is the average 10-year T-Bill

return between 1928 and 2010.

Risk premium is the difference between the expected return on stocks and the risk-free

rate of return. DOE used the average annual return on the S&P 500 between 1928 and 2010 as 

the expected return on stocks to arrive at an estimated market risk premium of 6.1 percent. 

Beta (β) is the correlation between the movement in the price of the stock and that of the 

broader market. In this case, Beta equals one if the stock is perfectly correlated with the S&P 500 

market index. A Beta lower than one means the stock is less volatile than the market index.

Values for Beta are only available for publicly traded companies.

DOE used the capital asset pricing model to calculate the cost of equity for three publicly 

traded Ice Maker manufacturers whose combined market share is over 90 percent. DOE 

determined that the industry-average cost of equity for the Automatic Commercial Ice Maker

industry is 16.7 percent (see Table 12.3.2). 

Table 12.3.2 Cost of Equity Calculation

Parameter

IndustryWeighted 

Average 

%

Manufacturer

A B C D

(1) Average Beta 1.9 1.5 n/a 1.7 2.0

(2) Yield on 10-Year 

T-Bill (1928-2010) 5.2 - - - -

(3) Market Risk Premium (1928-2010) 6.1 - - - -

Cost of Equity (2)+[(1)*(3)] 16.7 14.4 n/a 15.5 17.5

Equity/Total Capital 68.6 71.0 86.5 92.7 65.8

Bond ratings are a tool to measure default risk and arrive at a cost of debt. Each bond 

rating is associated with a particular spread. One way of estimating a company’s cost of debt is 

to treat it as a spread (usually expressed in basis points) over the risk-free rate. DOE used this 

12-9
method to calculate the cost of debt for three manufacturers by using S&P ratings and adding the 

relevant spread to the risk-free rate. 

Since proceeds from debt issuance are tax deductible, DOE adjusted the gross cost of 

debt by the industry-average tax rate to determine the net cost of debt for the industry. DOE 

determined that the after-tax industry-average cost of debt for the Automatic Commercial Ice 

Maker industry is 4.5 percent. Table 12.3.3 presents the derivation of the cost of debt and the 

capital structure of the industry (i.e. the debt ratio (debt/total capital)).

Table 12.3.3 Cost of Debt Calculation

Parameter

IndustryWeighted 

Average 

%

Manufacturer

A B C D

S&P Bond Rating -- BBB A AA BBB

(1) Yield on 10-Year T-Bill (1928-2010) 5.2 - - - -

(2) Gross Cost of Debt 6.8 6.8 6.2 5.9 6.8

(3) Tax Rate 33.3 42.6 25.4 14.0 30.7

Net Cost of Debt [(2) x ((1)-(3))] 4.5 - - - -

Debt/Total Capital 31.4 29.0 13.5 7.3 34.2

Correcting for an inflation rate of 3.1 percent over the analysis period, DOE’s calculated 

value for the Automatic Commercial Ice Maker industry’s inflation-adjusted WACC and the 

initial estimate of the discount rate is 8.1 percent. DOE adjusted this figure to 8.5 percent for the 

GRIM based on feedback received during manufacturer interviews.

12.3.5 Trial Standard Levels 

DOE developed TSLs to analyze the impact on manufacturers of new and amended

energy efficiency standards on the capacity groups of Automatic Commercial Ice Makers. To 

assess the impact of each TSL on INPV, DOE established 36 “capacity groups” for use in the 

GRIM. The capacity groups align with the capacity ranges used to define the piece-wise linear 

minimum energy consumption equations for each TSL. The capacity groups are more granular 

than the equipment classes defined in chapter 5 and are consistent with the groupings used in the 

engineering analysis. 

Table 12.3.4 provides a crosswalk between equipment classes and capacity groups and

also provides the minimum energy consumption equations for each TSL, as first defined in 

chapter 3.

12-10
Table 12.3.4 Crosswalk between Equipment Class, Capacity Group, and Capacity Group

Number

Equipment 

Class Capacity Group 

Capacit

y 

Group 

Numbe

r

Capacity Range 

lb/day TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

IMH-W-SmallB IMH-W-Small-B 01 <300 7.19 –

0.0055H

7.19 –

0.0055H

6.88 –

0.0055H

6.88 –

0.0055H

6.32 –

0.0055H

IMH-W-Med-B IMH-W-Medium-B 02 ≥300 and <850 6.28 –

0.00247H

6.28 –

0.00247H

5.8 –

0.00191H

5.9 –

0.00224H

5.17 –

0.00165H

IMH-W-LargeB IMH-W-Large-B 03 ≥850 and <1500 4.42 –

0.00028H

4.42 –

0.00028H

4.0 4.0

3.86 –

0.00012H

IMH-WExtended-B

IMH-W-Ext1-B 04 ≥1,500 and <2,600 4.0 4.0

3.62 + 

0.00004H

IMH-W-Ext2-B 05 ≥2,600 3.72

IMH-A-Small-B IMH-A-Small-B 06 <300 10.09 –

0.0106H

10.05 –

0.01173H

10 –

0.01233H

10 –

0.01233H

9.38 –

0.01233H

IMH-AMedium-B IMH-A-Medium-B 07 ≥300 and <800 7.81 –

0.003H

7.38 –

0.00284H

7.05 –

0.0025H

7.19 –

0.00298H

6.31 –

0.0021H

IMH-A-Large-B IMH-A-Large-B 08 ≥800 and <1,500 6.21 –

0.00099H

5.56 –

0.00056H

5.55 –

0.00063H

5.04 –

0.00029H

4.65 –

0.00003H

IMH-AExtended-B IMH-A-Ext-B 09 >1,500 4.73 4.72 4.61 4.61 4.61

RCU-NRCSmall-BRCU-NRC-SmallB 10 <1,000 7.97 –

0.00342H

7.97 –

0.00342H

7.97 –

0.00342H

7.52 –

0.00323H

7.35 –

0.00316H

RCU-NRCLarge-BRCU-NRC-LargeB 11 ≥1,000 and <1,500

4.55 4.55 4.55 4.29

4.19

RCU-NRCExtended-B

RCU-NRC-Ext1-B 12 ≥1,500 and < 2,400 3.93 + 

0.00017H

RCU-NRC-Ext2-B 13 ≥2,400 4.34

RCU-RC-SmallB RCU-RC-Small-B 14 <942 7.97 –

0.00342H

7.97 –

0.00342H

7.97 –

0.00342H

7.52 –

0.00323H

7.35 –

0.00316H

RCU-RC-LargeB RCU-RC-Large-B 15 ≥942 and < 1,500

4.75 4.75 4.75 4.49

4.39

RCU-RCExtended-B

RCU-RC-Ext1-B 16 ≥1,500 and < 2,400 4.13 + 

0.00017H

RCU-RC-Ext2-B 17 ≥2,400 4.54

SCU-W-SmallB SCU-W-Small-B 18 <200 10.64 –

0.019H

9.88 –

0.019H 9.5 – 0.019H 9.14 –0.019H

9.14 –

0.019H

SCU-W-LargeB SCU-W-Large-B 19 ≥200 6.84 6.08 5.7 5.34 5.34

SCU-A-Small-B SCU-A-Small-B 20 <110 16.72 –

0.0469H

15.43 –

0.0469H

14.79 –

0.0469H

14.15 –

0.0469H

13.76 –

0.0469H

SCU-A-Large-B SCU-A-Large-B 21 ≥110 and <200 14.91 –

0.03044H

13.24 –

0.027H

12.42 –

0.02533H

11.47 –

0.02256H 10.6 – 0.02

SCU-AExtended-B SCU-A-Ext-B 22 ≥200 8.82 7.84 7.35 6.96 6.96

IMH-W-SmallC IMH-W-Small-C 23 <801 7.29 –

0.003H

6.89 –

0.00283H

6.48 –

0.00267H

6.48 –

0.00267H

5.75 –

0.00237H

IMH-W-LargeC IMH-W-Large-C 24 ≥801 4.59 4.59 4.34 4.34 3.93

IMH-A-Small-C IMH-A-Small-C 25 <310 10.1 –

0.00629H

9.64 –

0.00629H

9.19 –

0.00629H

9.19 -

0.00629H

8.38 –

0.00629H

IMH-A-Large-C IMH-A-Large-C 26 ≥310 and <820 9.49 –

0.00433H

8.75 –

0.00343H

8.23 –

0.0032H

8.23 –

0.0032H

7.25 –

0.00265H

IMH-AExtended-C IMH-A-Ext-C 27 ≥820 5.94 5.94 5.61 5.61 5.08

RCU-NRCSmall-CRCU-NRC-SmallC 28 <800 9.85 –

0.00519H

9.78 –

0.0055H

9.7 –

0.0058H

9.7 –

0.0058H

9.26 –

0.0058H

RCU-NRCLarge-CRCU-NRC-LargeC 29 ≥800 5.7 5.38 5.06 5.06 4.62

RCU-RC-SmallC RCU-RC-Small-C 30 <800 10.05 –

0.00519H

9.98 –

0.0055H

9.9 –

0.0058H

9.9 –

0.0058H

9.46 –

0.0058H

RCU-RC-LargeC RCU-RC-Large-C 31 ≥800 5.9 5.58 5.26 5.26 4.82

SCU-W-SmallC SCU-W-Small-C 32 <900 8.55 –

0.0034H 8.08 0.0032H 7.6 –0.00302H

7.6 –

0.00302H

6.84 –

0.00272H

SCU-W-LargeC SCU-W-Large-C 33 ≥900 5.49 5.19 4.88 4.88 4.39

SCU-A-Small-C SCU-A-Small-C 34 <200 15.26 –

0.03

14.73 –

0.03H 14.22 -0.03H 14.22 –0.03H 13.4 – 0.03H

SCU-A-Large-C SCU-A-Large-C 35 ≥200 and 700 10.66 –

0.00702H

10.06 –

0.00663H

9.47 –

0.00624H

9.47 –

0.00624H

8.52 –

0.00562H

SCU-AExtended-C SCU-A-Ext-C 36 ≥700 5.75 5.42 5.1 5.1 4.59

12-11
12.3.6 NIA Shipment Forecast

The GRIM estimates manufacturer revenues based on total unit-shipment forecasts and 

the distribution of these values by efficiency level. Changes in the efficiency mix at each 

standard level are a key driver of manufacturer finances. For this analysis, the GRIM used the 

NIA’s annual shipment forecasts from 2015 through 2047. To estimate efficiency trends in the 

standards cases, DOE uses a “roll-up” scenario in its standards rulemakings. Under the roll-up 

scenario, DOE assumes that equipment efficiencies in the base case that do not meet the standard 

level under consideration would “roll up” to the efficiency level that just meets the level set by 

the standard, and equipment already being purchased at efficiencies at or above the standard 

level would be unaffected. The assumptions and methodology that drive this analysis are 

described in chapter 9 of the TSD.

12.3.7 Production Costs

Changes in the MPCs of Automatic Commercial Ice Makers can affect revenues, gross 

margins, and cash flow of the industry, making production cost data key GRIM inputs for DOE’s 

analysis. In the engineering analysis, DOE created cost curves for each equipment class using 

data from its reverse engineering analysis to develop both the baseline MPCs and the incremental 

costs that correspond to the design options. Generally, manufacturing higher efficiency products 

is more costly than manufacturing baseline products due to the use of more complex components 

and higher-cost raw materials.

The cost model disaggregated the MPCs at each efficiency level into material, labor, 

overhead, and depreciation. For materials, DOE used the incremental component and raw 

material costs that correspond to the design options at each EL. For labor, DOE estimated the 

labor contribution at each EL by examining how the design options may influence manufacturing 

and assembly practices. For depreciation, DOE used a depreciation value that is consistent with 

historical information in SEC 10-Ks. The remainder of total overhead was allocated to factory 

overhead. 

Later, manufacturers validated these estimates and assumptions during interviews. DOE 

used the resulting MPCs and cost breakdowns as described in section 12.3.2.4 above, and further 

detailed in chapter 5 of the TSD, for each efficiency level analyzed in the GRIM analysis. An 

additional efficiency level, 3A, was necessary for several equipment classes. For analytical 

consistency, DOE included this level in all of the capacity groups. Efficiency level 3A was 

derived by averaging the values in efficiency levels three and four. TSD Chapter 5 contains 

detailed information on the efficiency levels used in this analysis.

The MSP is comprised of production costs (the direct manufacturing costs or MPCs), nonproduction costs (indirect costs like SG&A), and profit. DOE calculated the MSPs for Automatic 

Commercial Ice Makers by multiplying the MPCs by the appropriate manufacturer markup for 

that product. Table 12.3.5 through Table 12.3.40 show the production cost estimates used in the 

GRIM for the representative equipment classes for Automatic Commercial Ice Makers. 

12-12
Table 12.3.5 Manufacturer Production Cost Breakdown (2013$) for IMH-W-Small-B

EL Materials Labor Depreciation Overhead MPC Markup MSP

EL 1 $764.09 $49.75 $23.35 $60.79 $897.98 1.25 $1,122.48

EL 1 $807.70 $49.72 $24.48 $59.70 $941.59 1.25 $1,176.99

EL 3 $843.25 $49.74 $25.41 $58.72 $977.12 1.25 $1,221.40

EL 3A $875.95 $49.90 $26.26 $57.98 $1,010.10 1.25 $1,262.62

EL 4 $908.93 $49.96 $27.12 $57.06 $1,043.07 1.25 $1,303.84

EL 5 $1,129.92 $56.55 $33.04 $51.21 $1,270.71 1.25 $1,588.39

Table 12.3.6 Manufacturer Production Cost Breakdown (2013$) for IMH-W-Medium-B

EL Materials Labor Depreciation Overhead MPC Markup MSP

EL 1 $1,180.60 $80.04 $40.17 $244.28 $1,545.08 1.25 $1,931.35

EL 1 $1,247.46 $80.11 $41.91 $242.43 $1,611.91 1.25 $2,014.89

EL 3 $1,384.50 $80.29 $45.48 $238.94 $1,749.21 1.25 $2,186.51

EL 3A $1,484.24 $82.41 $48.15 $237.14 $1,851.94 1.25 $2,314.93

EL 4 $1,586.02 $84.25 $50.82 $233.58 $1,954.67 1.25 $2,443.34

Table 12.3.7 Manufacturer Production Cost Breakdown (2013$) for IMH-W-Large-B

EL Materials Labor Depreciation Overhead MPC Markup MSP

EL 1 $1,305.72 $88.52 $44.43 $270.17 $1,708.83 1.25 $2,136.04

EL 2 $1,719.46 $94.06 $55.33 $259.20 $2,128.04 1.25 $2,660.05

Table 12.3.8 Manufacturer Production Cost Breakdown (2013$) for IMH-W-Ext1-B

EL Materials Labor Depreciation Overhead MPC Markup MSP

EL 1 $2,861.98 $257.08 $97.44 $531.02 $3,747.52 1.25 $4,684.40

EL 2 $3,379.71 $265.40 $111.12 $517.56 $4,273.79 1.25 $5,342.24

Table 12.3.9 Manufacturer Production Cost Breakdown (2013$) for IMH-W-Ext2-B

EL Materials Labor Depreciation Overhead MPC Markup MSP

EL 1 $2,861.98 $257.08 $97.44 $531.02 $3,747.52 1.25 $4,684.40

EL 2 $3,379.71 $265.40 $111.12 $517.56 $4,273.79 1.25 $5,342.24

Table 12.3.10 Manufacturer Production Cost Breakdown (2013$) for IMH-A-Small-B

EL Materials Labor Depreciation Overhead MPC Markup MSP

EL 1 $778.84 $48.71 $23.80 $64.18 $915.53 1.25 $1,144.41

EL 2 $835.30 $52.08 $25.36 $62.62 $975.36 1.25 $1,219.20

EL 3 $864.01 $63.14 $26.39 $61.62 $1,015.17 1.25 $1,268.96

EL 3A $881.64 $69.86 $27.03 $61.02 $1,039.55 1.25 $1,299.44

EL 4 $892.84 $74.17 $27.43 $60.56 $1,054.99 1.25 $1,318.74

EL 5 $1,133.11 $80.02 $33.83 $54.12 $1,301.08 1.25 $1,626.35

EL 6 $1,136.02 $81.05 $33.93 $54.16 $1,305.17 1.25 $1,631.46

12-13
Table 12.3.11 Manufacturer Production Cost Breakdown (2013$) for IMH-A-Medium-B

EL Materials Labor Depreciation Overhead MPC Markup MSP

EL 1 $778.84 $48.71 $23.80 $64.18 $915.53 1.25 $1,144.41

EL 2 $835.30 $52.08 $25.36 $62.62 $975.36 1.25 $1,219.20

EL 3 $864.01 $63.14 $26.39 $61.62 $1,015.17 1.25 $1,268.96

EL 3A $881.64 $69.86 $27.03 $61.02 $1,039.55 1.25 $1,299.44

EL 4 $892.84 $74.17 $27.43 $60.56 $1,054.99 1.25 $1,318.74

EL 5 $1,133.11 $80.02 $33.83 $54.12 $1,301.08 1.25 $1,626.35

EL 6 $1,136.02 $81.05 $33.93 $54.16 $1,305.17 1.25 $1,631.46

Table 12.3.12 Manufacturer Production Cost Breakdown (2013$) for IMH-A-Large-B

EL Materials Labor Depreciation Overhead MPC Markup MSP

EL 1 $1,099.28 $115.63 $38.84 $240.23 $1,493.99 1.25 $1,867.49

EL 2 $1,144.05 $115.56 $40.01 $239.12 $1,538.73 1.25 $1,923.41

EL 3 $1,191.72 $115.65 $41.25 $237.80 $1,586.42 1.25 $1,983.03

EL 3A $1,195.05 $115.58 $41.33 $237.83 $1,589.80 1.25 $1,987.25

EL 4 $1,256.80 $115.79 $42.94 $236.20 $1,651.73 1.25 $2,064.66

EL 5 $1,476.35 $119.84 $48.76 $230.49 $1,875.44 1.25 $2,344.30

Table 12.3.13 Manufacturer Production Cost Breakdown (2013$) for IMH-A-Ext-B

EL Materials Labor Depreciation Overhead MPC Markup MSP

EL 1 $1,371.80 $80.29 $42.95 $156.94 $1,651.97 1.25 $2,064.96

EL 2 $1,793.68 $86.09 $54.06 $145.56 $2,079.39 1.25 $2,599.24

EL 3 $1,839.84 $86.32 $55.28 $144.57 $2,126.00 1.25 $2,657.50

Table 12.3.14 Manufacturer Production Cost Breakdown (2013$) for RCU-NRC-Small-B

EL Materials Labor Depreciation Overhead MPC Markup MSP

EL 1 $1,572.99 $111.72 $51.23 $234.48 $1,970.43 1.25 $2,463.04

EL 2 $1,632.22 $111.85 $52.78 $233.03 $2,029.87 1.25 $2,537.34

EL 3 $1,825.18 $113.68 $57.84 $228.04 $2,224.74 1.25 $2,780.93

EL 3A $1,929.33 $115.88 $60.62 $225.82 $2,331.66 1.25 $2,914.57

EL 4 $2,034.99 $117.78 $63.40 $222.40 $2,438.57 1.25 $3,048.21

Table 12.3.15 Manufacturer Production Cost Breakdown (2013$) for RCU-NRC-Large-B

EL Materials Labor Depreciation Overhead MPC Markup MSP

EL 1 $1,572.99 $111.72 $51.23 $234.48 $1,970.43 1.25 $2,463.04

EL 2 $1,632.22 $111.85 $52.78 $233.03 $2,029.87 1.25 $2,537.34

EL 3 $1,825.18 $113.68 $57.84 $228.04 $2,224.74 1.25 $2,780.93

EL 3A $1,929.33 $115.88 $60.62 $225.82 $2,331.66 1.25 $2,914.57

EL 4 $2,034.99 $117.78 $63.40 $222.40 $2,438.57 1.25 $3,048.21

12-14
Table 12.3.16 Manufacturer Production Cost Breakdown (2013$) for RCU-NRC-Ext1-B

EL Materials Labor Depreciation Overhead MPC Markup MSP

EL 1 $2,625.10 $256.84 $83.27 $237.31 $3,202.51 1.25 $4,003.14

EL 2 $2,909.66 $257.38 $90.68 $229.83 $3,487.55 1.25 $4,359.44

EL 3 $3,412.14 $265.49 $103.96 $216.71 $3,998.29 1.25 $4,997.86

Table 12.3.17 Manufacturer Production Cost Breakdown (2013$) for RCU-NRC-Ext1-B2

EL Materials Labor Depreciation Overhead MPC Markup MSP

EL 1 $2,625.10 $256.84 $83.27 $237.31 $3,202.51 1.25 $4,003.14

EL 2 $2,909.66 $257.38 $90.68 $229.83 $3,487.55 1.25 $4,359.44

EL 3 $3,412.14 $265.49 $103.96 $216.71 $3,998.29 1.25 $4,997.86

Table 12.3.18 Manufacturer Production Cost Breakdown (2013$) for RCU-RC-Small-B

EL Materials Labor Depreciation Overhead MPC Markup MSP

EL 1 $1,572.99 $111.72 $51.23 $234.48 $1,970.43 1.25 $2,463.04

EL 2 $1,632.22 $111.85 $52.78 $233.03 $2,029.87 1.25 $2,537.34

EL 3 $1,825.18 $113.68 $57.84 $228.04 $2,224.74 1.25 $2,780.93

EL 3A $1,929.33 $115.88 $60.62 $225.82 $2,331.66 1.25 $2,914.57

EL 4 $2,034.99 $117.78 $63.40 $222.40 $2,438.57 1.25 $3,048.21

Table 12.3.19 Manufacturer Production Cost Breakdown (2013$) for RCU-RC-Large-B

EL Materials Labor Depreciation Overhead MPC Markup MSP

EL 1 $1,572.99 $111.72 $51.23 $234.48 $1,970.43 1.25 $2,463.04

EL 2 $1,632.22 $111.85 $52.78 $233.03 $2,029.87 1.25 $2,537.34

EL 3 $1,825.18 $113.68 $57.84 $228.04 $2,224.74 1.25 $2,780.93

EL 3A $1,929.33 $115.88 $60.62 $225.82 $2,331.66 1.25 $2,914.57

EL 4 $2,034.99 $117.78 $63.40 $222.40 $2,438.57 1.25 $3,048.21

Table 12.3.20 Manufacturer Production Cost Breakdown (2013$) for RCU-RC-Ext1-B

EL Materials Labor Depreciation Overhead MPC Markup MSP

EL 1 $2,625.10 $256.84 $83.27 $237.31 $3,202.51 1.25 $4,003.14

EL 2 $2,909.66 $257.38 $90.68 $229.83 $3,487.55 1.25 $4,359.44

EL 3 $3,412.14 $265.49 $103.96 $216.71 $3,998.29 1.25 $4,997.86

Table 12.3.21 Manufacturer Production Cost Breakdown (2013$) for RCU-RC-Ext2-B

EL Materials Labor Depreciation Overhead MPC Markup MSP

EL 1 $2,625.10 $256.84 $83.27 $237.31 $3,202.51 1.25 $4,003.14

EL 2 $2,909.66 $257.38 $90.68 $229.83 $3,487.55 1.25 $4,359.44

EL 3 $3,412.14 $265.49 $103.96 $216.71 $3,998.29 1.25 $4,997.86

12-15
Table 12.3.22 Manufacturer Production Cost Breakdown (2013$) for SCU-W-Small-B

EL Materials Labor Depreciation Overhead MPC Markup MSP

EL 1 $953.13 $63.17 $31.83 $176.18 $1,224.31 1.25 $1,530.39

EL 2 $993.77 $67.26 $32.99 $175.00 $1,269.02 1.25 $1,586.28

EL 3 $1,011.31 $73.97 $33.62 $174.33 $1,293.24 1.25 $1,616.55

EL 3A $1,020.51 $76.96 $33.94 $174.09 $1,305.50 1.25 $1,631.88

EL 4 $1,029.70 $79.99 $34.26 $173.81 $1,317.76 1.25 $1,647.20

EL 5 $1,056.97 $80.02 $34.97 $172.96 $1,344.92 1.25 $1,681.15

EL 6 $1,292.94 $84.17 $41.21 $166.75 $1,585.07 1.25 $1,981.34

Table 12.3.23 Manufacturer Production Cost Breakdown (2013$) for SCU-W-Large-B

EL Materials Labor Depreciation Overhead MPC Markup MSP

EL 1 $953.13 $63.17 $31.83 $176.18 $1,224.31 1.25 $1,530.39

EL 2 $993.77 $67.26 $32.99 $175.00 $1,269.02 1.25 $1,586.28

EL 3 $1,011.31 $73.97 $33.62 $174.33 $1,293.24 1.25 $1,616.55

EL 3A $1,020.51 $76.96 $33.94 $174.09 $1,305.50 1.25 $1,631.88

EL 4 $1,029.70 $79.99 $34.26 $173.81 $1,317.76 1.25 $1,647.20

EL 5 $1,056.97 $80.02 $34.97 $172.96 $1,344.92 1.25 $1,681.15

EL 6 $1,292.94 $84.17 $41.21 $166.75 $1,585.07 1.25 $1,981.34

Table 12.3.24 Manufacturer Production Cost Breakdown (2013$) for SCU-A-Small-B

EL Materials Labor Depreciation Overhead MPC Markup MSP

EL 1 $729.69 $80.12 $27.34 $214.28 $1,051.42 1.25 $1,314.28

EL 2 $754.09 $80.15 $27.97 $213.67 $1,075.89 1.25 $1,344.86

EL 3 $768.17 $80.11 $28.34 $213.30 $1,089.91 1.25 $1,362.39

EL 3A $775.14 $80.13 $28.52 $213.13 $1,096.93 1.25 $1,371.16

EL 4 $782.14 $80.15 $28.70 $212.95 $1,103.94 1.25 $1,379.93

EL 5 $826.93 $80.18 $29.87 $211.70 $1,148.67 1.25 $1,435.84

EL 6 $877.19 $80.09 $31.17 $210.54 $1,199.00 1.25 $1,498.75

EL 7 $1,092.34 $84.24 $36.87 $204.80 $1,418.26 1.25 $1,772.83

Table 12.3.25 Manufacturer Production Cost Breakdown (2013$) for SCU-A-Large-B

EL Materials Labor Depreciation Overhead MPC Markup MSP

EL 1 $763.17 $90.39 $29.16 $238.76 $1,121.48 1.25 $1,401.85

EL 2 $792.30 $90.44 $29.92 $237.94 $1,150.59 1.25 $1,438.24

EL 3 $808.09 $90.40 $30.33 $237.60 $1,166.41 1.25 $1,458.01

EL 3A $821.69 $90.39 $30.68 $237.31 $1,180.08 1.25 $1,475.09

EL 4 $835.38 $90.37 $31.04 $236.96 $1,193.74 1.25 $1,492.18

EL 5 $870.04 $90.41 $31.94 $235.97 $1,228.35 1.25 $1,535.44

EL 6 $1,107.64 $94.53 $38.22 $229.79 $1,470.19 1.25 $1,837.74

12-16
Table 12.3.26 Manufacturer Production Cost Breakdown (2013$) for SCU-A-Ext-B

EL Materials Labor Depreciation Overhead MPC Markup MSP

EL 1 $763.17 $90.39 $29.16 $238.76 $1,121.48 1.25 $1,401.85

EL 2 $792.30 $90.44 $29.92 $237.94 $1,150.59 1.25 $1,438.24

EL 3 $808.09 $90.40 $30.33 $237.60 $1,166.41 1.25 $1,458.01

EL 3A $821.69 $90.39 $30.68 $237.31 $1,180.08 1.25 $1,475.09

EL 4 $835.38 $90.37 $31.04 $236.96 $1,193.74 1.25 $1,492.18

EL 5 $870.04 $90.41 $31.94 $235.97 $1,228.35 1.25 $1,535.44

EL 6 $1,107.64 $94.53 $38.22 $229.79 $1,470.19 1.25 $1,837.74

Table 12.3.27 Manufacturer Production Cost Breakdown (2013$) for IMH-W-Small-C

EL Materials Labor Depreciation Overhead MPC Markup MSP

EL 1 $1,991.28 $123.64 $70.34 $520.28 $2,705.55 1.25 $3,381.94

EL 2 $2,023.84 $123.50 $71.19 $519.72 $2,738.25 1.25 $3,422.81

EL 3 $2,045.13 $123.63 $71.75 $519.08 $2,759.59 1.25 $3,449.49

EL 3A $2,056.23 $123.57 $72.04 $518.80 $2,770.64 1.25 $3,463.30

EL 4 $2,067.35 $123.51 $72.32 $518.51 $2,781.69 1.25 $3,477.11

EL 5 $2,380.90 $123.50 $80.48 $510.41 $3,095.29 1.25 $3,869.11

EL 6 $2,384.50 $123.64 $80.57 $510.06 $3,098.76 1.25 $3,873.45

Table 12.3.28 Manufacturer Production Cost Breakdown (2013$) for IMH-W-Large-C

EL Materials Labor Depreciation Overhead MPC Markup MSP

EL 1 $1,565.93 $136.71 $54.35 $333.42 $2,090.41 1.25 $2,613.01

EL 2 $1,595.83 $136.77 $55.13 $332.69 $2,120.42 1.25 $2,650.53

EL 3 $1,614.26 $136.66 $55.61 $332.13 $2,138.66 1.25 $2,673.33

EL 3A $1,829.43 $137.85 $61.32 $329.83 $2,358.42 1.25 $2,948.03

EL 4 $2,053.78 $136.64 $67.03 $320.73 $2,578.18 1.25 $3,222.73

EL 5 $2,067.73 $136.86 $67.40 $320.13 $2,592.12 1.25 $3,240.15

Table 12.3.29 Manufacturer Production Cost Breakdown (2013$) for IMH-A-Small-C

EL Materials Labor Depreciation Overhead MPC Markup MSP

EL 1 $1,991.28 $123.64 $70.34 $520.28 $2,705.55 1.25 $3,381.94

EL 2 $2,023.84 $123.50 $71.19 $519.72 $2,738.25 1.25 $3,422.81

EL 3 $2,045.13 $123.63 $71.75 $519.08 $2,759.59 1.25 $3,449.49

EL 3A $2,056.23 $123.57 $72.04 $518.80 $2,770.64 1.25 $3,463.30

EL 4 $2,067.35 $123.51 $72.32 $518.51 $2,781.69 1.25 $3,477.11

EL 5 $2,380.90 $123.50 $80.48 $510.41 $3,095.29 1.25 $3,869.11

EL 6 $2,384.50 $123.64 $80.57 $510.06 $3,098.76 1.25 $3,873.45

12-17
Table 12.3.30 Manufacturer Production Cost Breakdown (2013$) for IMH-A-Large-C

EL Materials Labor Depreciation Overhead MPC Markup MSP

EL 1 $1,991.28 $123.64 $70.34 $520.28 $2,705.55 1.25 $3,381.94

EL 2 $2,023.84 $123.50 $71.19 $519.72 $2,738.25 1.25 $3,422.81

EL 3 $2,045.13 $123.63 $71.75 $519.08 $2,759.59 1.25 $3,449.49

EL 3A $2,056.23 $123.57 $72.04 $518.80 $2,770.64 1.25 $3,463.30

EL 4 $2,067.35 $123.51 $72.32 $518.51 $2,781.69 1.25 $3,477.11

EL 5 $2,380.90 $123.50 $80.48 $510.41 $3,095.29 1.25 $3,869.11

EL 6 $2,384.50 $123.64 $80.57 $510.06 $3,098.76 1.25 $3,873.45

Table 12.3.31 Manufacturer Production Cost Breakdown (2013$) for IMH-A-Ext-C

EL Materials Labor Depreciation Overhead MPC Markup MSP

EL 1 $1,565.93 $136.71 $54.35 $333.42 $2,090.41 1.25 $2,613.01

EL 2 $1,595.83 $136.77 $55.13 $332.69 $2,120.42 1.25 $2,650.53

EL 3 $1,614.26 $136.66 $55.61 $332.13 $2,138.66 1.25 $2,673.33

EL 3A $1,829.43 $137.85 $61.32 $329.83 $2,358.42 1.25 $2,948.03

EL 4 $2,053.78 $136.64 $67.03 $320.73 $2,578.18 1.25 $3,222.73

EL 5 $2,067.73 $136.86 $67.40 $320.13 $2,592.12 1.25 $3,240.15

Table 12.3.32 Manufacturer Production Cost Breakdown (2013$) for RCU-NRC-Small-C

EL Materials Labor Depreciation Overhead MPC Markup MSP

EL 1 $1,565.93 $136.71 $54.35 $333.42 $2,090.41 1.25 $2,613.01

EL 2 $1,595.62 $136.75 $55.12 $332.65 $2,120.15 1.25 $2,650.19

EL 3 $1,637.54 $136.86 $56.21 $331.44 $2,162.05 1.25 $2,702.56

EL 3A $1,652.69 $136.83 $56.61 $331.04 $2,177.17 1.25 $2,721.46

EL 4 $1,667.89 $136.80 $57.00 $330.60 $2,192.28 1.25 $2,740.35

EL 5 $2,087.80 $136.63 $67.92 $320.01 $2,612.36 1.25 $3,265.45

EL 6 $2,104.95 $136.74 $68.37 $319.49 $2,629.54 1.25 $3,286.93

12-18
Table 12.3.33 Manufacturer Production Cost Breakdown (2013$) for RCU-NRC-Large-C

EL Materials Labor Depreciation Overhead MPC Markup MSP

EL 1 $1,565.93 $136.71 $54.35 $333.42 $2,090.41 1.25 $2,613.01

EL 2 $1,595.62 $136.75 $55.12 $332.65 $2,120.15 1.25 $2,650.19

EL 3 $1,637.54 $136.86 $56.21 $331.44 $2,162.05 1.25 $2,702.56

EL 3A $1,652.69 $136.83 $56.61 $331.04 $2,177.17 1.25 $2,721.46

EL 4 $1,667.89 $136.80 $57.00 $330.60 $2,192.28 1.25 $2,740.35

EL 5 $2,087.80 $136.63 $67.92 $320.01 $2,612.36 1.25 $3,265.45

EL 6 $2,104.95 $136.74 $68.37 $319.49 $2,629.54 1.25 $3,286.93

Table 12.3.34 Manufacturer Production Cost Breakdown (2013$) for RCU-RC-Small-C

EL Materials Labor Depreciation Overhead MPC Markup MSP

EL 1 $1,565.93 $136.71 $54.35 $333.42 $2,090.41 1.25 $2,613.01

EL 2 $1,595.62 $136.75 $55.12 $332.65 $2,120.15 1.25 $2,650.19

EL 3 $1,637.54 $136.86 $56.21 $331.44 $2,162.05 1.25 $2,702.56

EL 3A $1,652.69 $136.83 $56.61 $331.04 $2,177.17 1.25 $2,721.46

EL 4 $1,667.89 $136.80 $57.00 $330.60 $2,192.28 1.25 $2,740.35

EL 5 $2,087.80 $136.63 $67.92 $320.01 $2,612.36 1.25 $3,265.45

EL 6 $2,104.95 $136.74 $68.37 $319.49 $2,629.54 1.25 $3,286.93

Table 12.3.35 Manufacturer Production Cost Breakdown (2013$) for RCU-RC-Large-C

EL Materials Labor Depreciation Overhead MPC Markup MSP

EL 1 $1,565.93 $136.71 $54.35 $333.42 $2,090.41 1.25 $2,613.01

EL 2 $1,595.62 $136.75 $55.12 $332.65 $2,120.15 1.25 $2,650.19

EL 3 $1,637.54 $136.86 $56.21 $331.44 $2,162.05 1.25 $2,702.56

EL 3A $1,652.69 $136.83 $56.61 $331.04 $2,177.17 1.25 $2,721.46

EL 4 $1,667.89 $136.80 $57.00 $330.60 $2,192.28 1.25 $2,740.35

EL 5 $2,087.80 $136.63 $67.92 $320.01 $2,612.36 1.25 $3,265.45

EL 6 $2,104.95 $136.74 $68.37 $319.49 $2,629.54 1.25 $3,286.93

Table 12.3.36 Manufacturer Production Cost Breakdown (2013$) for SCU-W-Small-C

EL Materials Labor Depreciation Overhead MPC Markup MSP

EL 1 $1,103.58 $93.13 $36.69 $177.65 $1,411.05 1.25 $1,763.81

EL 2 $1,117.75 $93.06 $37.05 $177.29 $1,425.16 1.25 $1,781.45

EL 3 $1,132.53 $93.17 $37.44 $176.83 $1,439.96 1.25 $1,799.95

EL 3A $1,144.45 $93.14 $37.75 $176.55 $1,451.89 1.25 $1,814.86

EL 4 $1,156.41 $93.10 $38.06 $176.24 $1,463.81 1.25 $1,829.76

EL 5 $1,481.09 $93.18 $46.50 $167.77 $1,788.54 1.25 $2,235.68

EL 6 $1,485.67 $93.07 $46.62 $167.84 $1,793.20 1.25 $2,241.50

12-19
Table 12.3.37 Manufacturer Production Cost Breakdown (2013$) for SCU-W-Large-C

EL Materials Labor Depreciation Overhead MPC Markup MSP

EL 1 $1,103.58 $93.13 $36.69 $177.65 $1,411.05 1.25 $1,763.81

EL 2 $1,117.75 $93.06 $37.05 $177.29 $1,425.16 1.25 $1,781.45

EL 3 $1,132.53 $93.17 $37.44 $176.83 $1,439.96 1.25 $1,799.95

EL 3A $1,144.45 $93.14 $37.75 $176.55 $1,451.89 1.25 $1,814.86

EL 4 $1,156.41 $93.10 $38.06 $176.24 $1,463.81 1.25 $1,829.76

EL 5 $1,481.09 $93.18 $46.50 $167.77 $1,788.54 1.25 $2,235.68

EL 6 $1,485.67 $93.07 $46.62 $167.84 $1,793.20 1.25 $2,241.50

Table 12.3.38 Manufacturer Production Cost Breakdown (2013$) for SCU-A-Small-C

EL Materials Labor Depreciation Overhead MPC Markup MSP

EL 1 $1,103.58 $93.13 $36.69 $177.65 $1,411.05 1.25 $1,763.81

EL 2 $1,117.75 $93.06 $37.05 $177.29 $1,425.16 1.25 $1,781.45

EL 3 $1,132.53 $93.17 $37.44 $176.83 $1,439.96 1.25 $1,799.95

EL 3A $1,144.45 $93.14 $37.75 $176.55 $1,451.89 1.25 $1,814.86

EL 4 $1,156.41 $93.10 $38.06 $176.24 $1,463.81 1.25 $1,829.76

EL 5 $1,481.09 $93.18 $46.50 $167.77 $1,788.54 1.25 $2,235.68

EL 6 $1,485.67 $93.07 $46.62 $167.84 $1,793.20 1.25 $2,241.50

Table 12.3.39 Manufacturer Production Cost Breakdown (2013$) for SCU-A-Large-C

EL Materials Labor Depreciation Overhead MPC Markup MSP

EL 1 $1,103.58 $93.13 $36.69 $177.65 $1,411.05 1.25 $1,763.81

EL 2 $1,117.75 $93.06 $37.05 $177.29 $1,425.16 1.25 $1,781.45

EL 3 $1,132.53 $93.17 $37.44 $176.83 $1,439.96 1.25 $1,799.95

EL 3A $1,144.45 $93.14 $37.75 $176.55 $1,451.89 1.25 $1,814.86

EL 4 $1,156.41 $93.10 $38.06 $176.24 $1,463.81 1.25 $1,829.76

EL 5 $1,481.09 $93.18 $46.50 $167.77 $1,788.54 1.25 $2,235.68

EL 6 $1,485.67 $93.07 $46.62 $167.84 $1,793.20 1.25 $2,241.50

Table 12.3.40 Manufacturer Production Cost Breakdown (2013$) for SCU-A-Ext-C

EL Materials Labor Depreciation Overhead MPC Markup MSP

EL 1 $1,103.58 $93.13 $36.69 $177.65 $1,411.05 1.25 $1,763.81

EL 2 $1,117.75 $93.06 $37.05 $177.29 $1,425.16 1.25 $1,781.45

EL 3 $1,132.53 $93.17 $37.44 $176.83 $1,439.96 1.25 $1,799.95

EL 3A $1,144.45 $93.14 $37.75 $176.55 $1,451.89 1.25 $1,814.86

EL 4 $1,156.41 $93.10 $38.06 $176.24 $1,463.81 1.25 $1,829.76

EL 5 $1,481.09 $93.18 $46.50 $167.77 $1,788.54 1.25 $2,235.68

EL 6 $1,485.67 $93.07 $46.62 $167.84 $1,793.20 1.25 $2,241.50

12-20
12.3.8 Conversion Costs

New and amended energy conservation standards typically cause manufacturers to incur 

one-time conversion costs to bring their production facilities and product designs into 

compliance with new regulations. For the MIA, DOE classified these one-time conversion costs 

into two major groups: capital conversion costs and product conversion costs. Capital conversion 

costs are investments in property, plant, and equipment to adapt or change existing production 

facilities so that new product designs can be fabricated and assembled. Product conversion costs 

are investments in research, development, testing, marketing, and other non-capitalized costs 

focused on making product designs comply with the new and amended energy conservation 

standard. The following sections describe the inputs DOE used in the GRIM in greater detail.

12.3.8.1 Automatic Commercial Ice Maker Product and Capital Conversion Costs

DOE used confidential information received during manufacturer interviews to develop

estimates of industry conversion costs. Specifically, DOE estimated the industry-wide 

conversion costs required to meet each TSL, for each capacity group. DOE developed these 

estimates using a three step process:

1. DOE reviewed public information in the CEC, ENERGY STAR, CEE product 

databases, as well as manufacturer websites, to determine the total number of

products that would require a redesign for each capacity group, at each TSL. 

2. DOE used confidential manufacturer-supplied information to estimate the average 

cost to redesign a single non-compliant model for each capacity group at each TSL. 

3. For each capacity group, DOE multiplied the single-model redesign costs at each TSL 

by the number of models requiring redesign at each TSL.

The total industry product and capital conversion costs at each TSL are the sums of the 

capital and product conversion costs determined for each capacity group. Table 12.3.41 shows

DOE’s estimates of the industry wide product and capital conversion costs necessary for 

Automatic Commercial Ice Maker equipment classes at each TSL.

Table 12.3.41 Product and Capital Conversion Costs for Standard Ice Makers by TSL

Conversion Costs by TSL (2013$M)

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

Product Conversion $12.3 $18.1 $23.8 $28.1 $40.3

Capital Conversion Costs $0.2 $0.6 $1.3 $2.0 $3.9

Total Conversion Costs $12.6 $18.7 $25.1 $30.0 $44.1

12-21
12.3.9 Markup Scenarios

DOE modeled two standards-case markup scenarios to represent the uncertainty 

regarding the potential impacts on prices and profitability for manufacturers following the 

implementation of new and amended energy conservation standards: (1) a preservation of gross 

margin percentage markup scenario; and (2) a preservation of earnings before interest and taxes 

(EBIT) markup scenario. These scenarios lead to different markups values that, when applied to 

the MPCs, result in varying revenue and cash flow impacts. 

12.3.9.1 Preservation of Gross Margin Markup Scenario

The preservation of gross margin markup scenario assessed the lower bound of potential 

impacts (higher profitability). DOE modeled a preservation of gross margin percentage markup 

scenario, in which a uniform “gross margin percentage” markup is applied across all efficiency 

levels. In this scenario, DOE assumed that a manufacturer’s absolute dollar markup would 

increase as production costs increase in the amended energy conservation standards case. 

Manufacturers have indicated that it is optimistic to assume that they would be able to maintain 

the same gross margin percentage markup as their production costs increase in response to a new

or amended energy conservation standard, particularly at higher TSLs. 

12.3.9.2 Preservation of EBIT Markup Scenario

The preservation of EBIT markup scenario assessed the upper bound of potential impacts 

(lower profitability). DOE modeled the preservation of the EBIT markup scenario, which 

assumes that manufacturers would not be able to preserve the same overall gross margin, but 

instead would lower their markup for marginally compliant products to maintain a costcompetitive product offering and keep the same overall level of EBIT as in the base case. Table 

12.3.42 through Table 12.3.78 lists the products DOE analyzed with the corresponding markups 

at each TSL for Automatic Commercial Ice Makers.

Table 12.3.42 Preservation of Gross Margin Markups for Automatic Commercial Ice 

Makers, Representative of all Equipment Classes

EL (EAEU) Markups by EL

EL 1 EL 2 EL 3 EL 3A EL 4 EL 5 EL 6 EL 7

EL 1 1.250

EL 2 1.250 1.250

EL 3 1.250 1.250 1.250

EL 3A 1.250 1.250 1.250 1.250

EL 4 1.250 1.250 1.250 1.250 1.250

EL 5 1.250 1.250 1.250 1.250 1.250 1.250

EL 6 1.250 1.250 1.250 1.250 1.250 1.250 1.250

EL 7 1.250 1.250 1.250 1.250 1.250 1.250 1.250 1.250

12-22
Table 12.3.43 Preservation of EBIT Markups for IMH-W-Small-B

EL (EAEU) Markups by EL

EL 1 EL 2 EL 3 EL 3A EL 4 EL 5

EL 1 1.250

EL 2 1.250 1.249

EL 3 1.250 1.250 1.248

EL 3A 1.250 1.250 1.250 0.000

EL 4 1.250 1.250 1.250 1.250 0.000

EL 5 1.250 1.250 1.250 1.250 1.250 1.238

Table 12.3.44 Preservation of EBIT Markups for IMH-W-Medium-B

EL (EAEU) Markups by EL 

EL 1 EL 2 EL 3 EL 3A EL 4

EL 1 1.250

EL 2 1.250 1.249

EL 3 1.250 1.250 1.247

EL 3A 1.250 1.250 1.250 0.000

EL 4 1.250 1.250 1.250 1.250 1.243

Table 12.3.45 Preservation of EBIT for IMH-W-Large-B

EL (EAEU) Markups by EL

EL 1 EL 2

EL 1 1.250

EL 2 1.250 1.245

Table 12.3.46 Preservation of EBIT Markups for IMH-W-Ext1-B

EL (EAEU) Markups by EL

EL 1 EL 2

EL 1 1.250

EL 2 1.250 1.245

Table 12.3.47 Preservation of EBIT Markups for IMH-W-Ext2-B

EL (EAEU) Markups by EL

EL 1 EL 2

EL 1 1.250

EL 2 1.250 1.245

12-23
Table 12.3.48 Preservation of EBIT Markups for IMH-A-Small-B

EL 

(EAEU)

Markups by EL

EL 1 EL 2 EL 3 EL 3A EL 4 EL 5 El 6

EL 1 1.250

EL 2 1.250 1.249

EL 3 1.250 1.250 1.248

EL 3A 1.250 1.250 1.250 1.247

EL 4 1.250 1.250 1.250 1.250 0.000

EL 5 1.250 1.250 1.250 1.250 1.250 0.000

El 6 1.250 1.250 1.250 1.250 1.250 1.250 1.238

Table 12.3.49 Preservation of EBIT Markups for IMH-A-Medium-B

EL (EAEU) Markups by EL

EL 1 EL 2 EL 3 EL 3A EL 4 EL 5 EL 6

EL 1 1.250

EL 2 1.250 1.249

EL 3 1.250 1.250 1.248

EL 3A 1.250 1.250 1.250 1.247

EL 4 1.250 1.250 1.250 1.250 0.000

EL 5 1.250 1.250 1.250 1.250 1.250 0.000

EL 6 1.250 1.250 1.250 1.250 1.250 1.250 1.238

Table 12.3.50 Preservation of EBIT Markups for IMH-A-Large-B

EL (EAEU) Markups by EL

EL 1 EL 2 EL 3 EL 3A EL 4 EL 5

EL 1 1.250

EL 2 1.250 1.249

EL 3 1.250 1.250 1.249

EL 3A 1.250 1.250 1.250 1.249

EL 4 1.250 1.250 1.250 1.250 1.247

EL 5 1.250 1.250 1.250 1.250 1.250 1.242

Table 12.3.51 Preservation of EBIT Markups for IMH-A-Ext-B

EL (EAEU) Markups by EL

EL 1 EL 2 EL 3

EL 1 1.250

EL 2 1.250 1.2448

EL 3 1.250 1.250 1.2465

12-24
Table 12.3.52 Preservation of EBIT Markups for RCU-NRC-Small-B

EL (EAEU) Markups by EL

EL 1 EL 2 EL 3 EL 3A EL 4

EL 1 1.250

EL 2 1.250 1.249

EL 3 1.250 1.250 1.246

EL 3A 1.250 1.250 1.250 0.000

EL 4 1.250 1.250 1.250 1.250 1.243

Table 12.3.53 Preservation of EBIT Markups for RCU-NRC-Large-B

EL (EAEU) Markups by EL

EL 1 EL 2 EL 3 EL 3A EL 4

EL 1 1.250

EL 2 1.250 1.249

EL 3 1.250 1.250 1.247

EL 3A 1.250 1.250 1.250 0.000

EL 4 1.250 1.250 1.250 1.250 1.243

Table 12.3.54 Preservation of EBIT Markups for RCU-NRC-Ext1-B

EL (EAEU) Markups by EL

EL 1 EL 2 EL 3

EL 1 1.250

EL 2 1.250 1.248

EL 3 1.250 1.250 1.244

Table 12.3.55 Preservation of EBIT Markups for RCU-NRC-Ext1-B2

EL (EAEU) Markups by EL

EL 1 EL 2 EL 3

EL 1 1.250

EL 2 1.250 1.248

EL 3 1.250 1.250 1.244

Table 12.3.56 Preservation of EBIT Markups for RCU-RC-Small-B

EL (EAEU) Markups by EL

EL 1 EL 2 EL 3 EL 3A EL 4

EL 1 1.250

EL 2 1.250 1.249

EL 3 1.250 1.250 1.246

EL 3A 1.250 1.250 1.250 0.000

EL 4 1.250 1.250 1.250 1.250 1.243

12-25
Table 12.3.57 Preservation of EBIT Markups for RCU-RC-Large-B

EL (EAEU) Markups by EL

EL 1 EL 2 EL 3 EL 3A EL 4

EL 1 1.250

EL 2 1.250 1.249

EL 3 1.250 1.250 1.246

EL 3A 1.250 1.250 1.250 0.000

EL 4 1.250 1.250 1.250 1.250 1.242

Table 12.3.58 Preservation of EBIT Markups for RCU-RC-Ext1-B

EL (EAEU) Markups by EL

EL 1 EL 2 EL 3

EL 1 1.250

EL 2 1.250 1.248

EL 3 1.250 1.250 1.244

Table 12.3.59 Preservation of EBIT Markups for RCU-RC-Ext2-B

EL (EAEU) Markups by EL

EL 1 EL 2 EL 3

EL 1 1.250

EL 2 1.250 1.250

EL 3 1.250 1.250 1.250

Table 12.3.60 Preservation of EBIT Markups for SCU-W-Small-B

EL (EAEU) Markups by EL

EL 1 EL 2 EL 3 EL 3A EL 4 EL 5 EL 6

EL 1 1.250

EL 2 1.250 1.248

EL 3 1.250 1.250 0.000

EL 3A 1.250 1.250 1.250 0.000

EL 4 1.250 1.250 1.250 1.250 1.247

EL 5 1.250 1.250 1.250 1.250 1.250 1.246

EL 6 1.250 1.250 1.250 1.250 1.250 1.250 1.240

12-26
Table 12.3.61 Preservation of EBIT Markups for SCU-W-Large-B

EL (EAEU) Markups by EL

EL 1 EL 2 EL 3 EL 3A EL 4 EL 5 EL 6

EL 1 1.250

EL 2 1.250 1.248

EL 3 1.250 1.250 1.250

EL 3A 1.250 1.250 1.250 1.250

EL 4 1.250 1.250 1.250 1.250 1.248

EL 5 1.250 1.250 1.250 1.250 1.250 1.247

EL 6 1.250 1.250 1.250 1.250 1.250 1.250 1.240

Table 12.3.62 Preservation of EBIT Markups for SCU-A-Small-B

EL 

(EAEU)

Markups by EL

EL 1 EL 2 EL 3 EL 3A EL 4 EL 5 EL 6 EL 7

EL 1 1.250

EL 2 1.250 1.249

EL 3 1.250 1.250 0.000

EL 3A 1.250 1.250 1.250 0.000

EL 4 1.250 1.250 1.250 1.250 1.249

EL 5 1.250 1.250 1.250 1.250 1.250 1.247

EL 6 1.250 1.250 1.250 1.250 1.250 1.250 1.245

EL 7 1.250 1.250 1.250 1.250 1.250 1.250 1.250 1.239

Table 12.3.63 Preservation of EBIT Markups for SCU-A-Large-B

EL 

(EAEU)

Markups by EL

EL 1 EL 2 EL 3 EL 3A EL 4 EL 5 EL 6

EL 1 1.250

EL 2 1.250 1.249

EL 3 1.250 1.250 0.000

EL 3A 1.250 1.250 1.250 0.000

EL 4 1.250 1.250 1.250 1.250 1.248

EL 5 1.250 1.250 1.250 1.250 1.250 1.247

EL 6 1.250 1.250 1.250 1.250 1.250 1.250 1.240

12-27
Table 12.3.64 Preservation of EBIT Markups for SCU-A-Ext-B

EL 

(EAEU)

Markups by EL

EL 1 EL 2 EL 3 EL 3A EL 4 EL 5 EL 6

EL 1 1.250

EL 2 1.250 1.249

EL 3 1.250 1.250 0.000

EL 3A 1.250 1.250 1.250 0.000

EL 4 1.250 1.250 1.250 1.250 1.248

EL 5 1.250 1.250 1.250 1.250 1.250 1.247

EL 6 1.250 1.250 1.250 1.250 1.250 1.250 1.239

Table 12.3.65 Preservation of EBIT Markups for IMH-W-Small-C

EL 

(EAEU)

Markups by EL

EL 1 EL 2 EL 3 EL 3A EL 4 EL 5 EL 6

EL 1 1.250

EL 2 1.250 1.250

EL 3 1.250 1.250 1.250

EL 3A 1.250 1.250 1.250 0.000

EL 4 1.250 1.250 1.250 1.250 1.250

EL 5 1.250 1.250 1.250 1.250 1.250 0.000

El 6 1.250 1.250 1.250 1.250 1.250 1.250 1.250

Table 12.3.66 Preservation of EBIT Markups for IMH-W-Large-C

EL (EAEU) Markups by EL

EL 1 EL 2 EL 3 EL 3A EL 4 EL 5

EL 1 1.250

EL 2 1.250 1.250

EL 3 1.250 1.250 1.250

EL 3A 1.250 1.250 1.250 1.250

EL 4 1.250 1.250 1.250 1.250 1.250

EL 5 1.250 1.250 1.250 1.250 1.250 1.250

12-28
Table 12.3.67 Preservation of EBIT Markups for IMH-A-Small-C

EL 

(EAEU)

Markups by EL

EL 1 EL 2 EL 3 EL 3A EL 4 EL 5 EL 6

EL 1 1.250

EL 2 1.250 1.250

EL 3 1.250 1.250 1.249

EL 3A 1.250 1.250 1.250 0.000

EL 4 1.250 1.250 1.250 1.250 1.249

EL 5 1.250 1.250 1.250 1.250 1.250 0.000

EL 6 1.250 1.250 1.250 1.250 1.250 1.250 1.245

Table 12.3.68 Preservation of EBIT Markups for IMH-A-Large-C

EL 

(EAEU)

Markups by EL

EL 1 EL 2 EL 3 EL 3A EL 4 EL 5 EL 6

EL 1 1.250

EL 2 1.250 1.250

EL 3 1.250 1.250 1.249

EL 3A 1.250 1.250 1.250 0.000

EL 4 1.250 1.250 1.250 1.250 1.249

EL 5 1.250 1.250 1.250 1.250 1.250 0.000

El 6 1.250 1.250 1.250 1.250 1.250 1.250 1.245

Table 12.3.69 Preservation of EBIT Markups for IMH-A-Ext-C

EL (EAEU) Markups by EL

EL 1 EL 2 EL 3 EL 3A EL 4 EL 5

EL 1 1.250

EL 2 1.250 1.250

EL 3 1.250 1.250 1.249

EL 3A 1.250 1.250 1.250 0.000

EL 4 1.250 1.250 1.250 1.250 0.000

EL 5 1.250 1.250 1.250 1.250 1.250 1.242

12-29
Table 12.3.70 Preservation of EBIT Markups for RCU-NRC-Small-C

EL 

(EAEU)

Markups by EL

EL 1 EL 2 EL 3 EL 3A EL 4 EL 5 EL 6

EL 1 1.250

EL 2 1.250 1.250

EL 3 1.250 1.250 1.249

EL 3A 1.250 1.250 1.250 0.000

EL 4 1.250 1.250 1.250 1.250 1.249

EL 5 1.250 1.250 1.250 1.250 1.250 0.000

EL 6 1.250 1.250 1.250 1.250 1.250 1.250 1.242

Table 12.3.71 Preservation of EBIT Markups for RCU-NRC-Large-C

EL (EAEU) Markups by EL

EL 1 EL 2 EL 3 EL 3A EL 4 EL 5 EL 6

EL 1 1.250

EL 2 1.250 1.250

EL 3 1.250 1.250 1.249

EL 3A 1.250 1.250 1.250 0.000

EL 4 1.250 1.250 1.250 1.250 1.249

EL 5 1.250 1.250 1.250 1.250 1.250 0.000

EL 6 1.250 1.250 1.250 1.250 1.250 1.250 1.242

Table 12.3.72 Preservation of EBIT Markups for RCU-RC-Small-C

EL 

(EAEU)

Markups by EL

EL 1 EL 2 EL 3 EL 3A EL 4 EL 5 EL 6

EL 1 1.250

EL 2 1.250 1.250

EL 3 1.250 1.250 1.249

EL 3A 1.250 1.250 1.250 0.000

EL 4 1.250 1.250 1.250 1.250 1.249

EL 5 1.250 1.250 1.250 1.250 1.250 0.000

EL 6 1.250 1.250 1.250 1.250 1.250 1.250 1.242

12-30
Table 12.3.73 Preservation of EBIT Markups for RCU-RC-Large-C

EL 

(EAEU)

Markups by EL

EL 1 EL 2 EL 3 EL 3A EL 4 EL 5 EL 6

EL 1 1.250

EL 2 1.250 1.250

EL 3 1.250 1.250 1.250

EL 3A 1.250 1.250 1.250 0.000

EL 4 1.250 1.250 1.250 1.250 1.250

EL 5 1.250 1.250 1.250 1.250 1.250 0.000

El 5 1.250 1.250 1.250 1.250 1.250 1.250 1.250

Table 12.3.74 Preservation of EBIT Markups for SCU-W-Small-C

EL (EAEU) Markups by EL

EL 1 EL 2 EL 3 EL 3A EL 4 EL 5 EL 6

EL 1 1.250

EL 2 1.250 1.250

EL 3 1.250 1.250 1.250

EL 3A 1.250 1.250 1.250 0.000

EL 4 1.250 1.250 1.250 1.250 1.250

EL 5 1.250 1.250 1.250 1.250 1.250 0.000

EL 6 1.250 1.250 1.250 1.250 1.250 1.250 1.250

Table 12.3.75 Preservation of EBIT Markups for SCU-W-Large-C

EL 

(EAEU)

Markups by EL

EL 1 EL 2 EL 3 EL 3A EL 4 EL 5 EL 6 EL 7

EL 1 1.250

EL 2 1.250 1.250

EL 3 1.250 1.250 1.250

EL 3A 1.250 1.250 1.250 0.000

EL 4 1.250 1.250 1.250 1.250 1.250

EL 5 1.250 1.250 1.250 1.250 1.250 0.000

EL 6 1.250 1.250 1.250 1.250 1.250 1.250 1.250

EL 7 1.250 1.250 1.250 1.250 1.250 1.250 1.250 0.000

12-31
Table 12.3.76 Preservation of EBIT Markups for SCU-A-Small-C

EL (EAEU) Markups by EL

EL 1 EL 2 EL 3 EL 3A EL 4 EL 5 EL 6

EL 1 1.250

EL 2 1.250 1.250

EL 3 1.250 1.250 1.249

EL 3A 1.250 1.250 1.250 0.000

EL 4 1.250 1.250 1.250 1.250 1.249

EL 5 1.250 1.250 1.250 1.250 1.250 0.000

EL 6 1.250 1.250 1.250 1.250 1.250 1.250 1.241

Table 12.3.77 Preservation of EBIT Markups for SCU-A-Large-C

EL (EAEU) Markups by EL

EL 1 EL 2 EL 3 EL 3A EL 4 EL 5 EL 6

EL 1 1.250

EL 2 1.250 1.250

EL 3 1.250 1.250 1.249

EL 3A 1.250 1.250 1.250 0.000

EL 4 1.250 1.250 1.250 1.250 1.249

EL 5 1.250 1.250 1.250 1.250 1.250 0.000

EL 6 1.250 1.250 1.250 1.250 1.250 1.250 1.241

Table 12.3.78 Preservation of EBIT Markups for SCU-A-Ext-C

EL 

(EAEU)

Markups by EL

EL 1 EL 2 EL 3 EL 3A EL 4 EL 5 EL 6

EL 1 1.250

EL 2 1.250 1.250

EL 3 1.250 1.250 1.250

EL 3A 1.250 1.250 1.250 0.000

EL 4 1.250 1.250 1.250 1.250 1.250

EL 5 1.250 1.250 1.250 1.250 1.250 0.000

EL 6 1.250 1.250 1.250 1.250 1.250 1.250 1.250

12.4 INDUSTRY FINANCIAL IMPACTS

Using the inputs and scenarios described in the previous sections, the GRIM estimated 

the financial impacts on the Automatic Commercial Ice Maker industry. The following sections 

detail additional inputs and assumptions for Automatic Commercial Ice Makers. The main 

12-32
results of the MIA are also reported in this section. The MIA consists of two key financial 

metrics: INPV and annual cash flows.

12.4.1 Impacts on Industry Net Present Value

The INPV measures the industry value and is used in the MIA to compare the economic 

impacts of different TSLs in the standards case. The INPV is different from DOE’s net present 

value, which is applied to the U.S. economy. The INPV is the sum of all net cash flows 

discounted at the industry’s cost of capital, or discount rate. The Automatic Commercial Ice 

Maker GRIM estimates cash flows from 2015 through 2047. This timeframe models both the 

short-term impacts on the industries from the announcement of the standard until the compliance 

date (2015 until an estimated compliance date of 2018) and a long-term assessment over the 30-

year analysis period used in the NIA (2018 – 2047).

In the MIA, DOE compares the INPV of the base case (no new and amended energy 

conservation standards) to that of each TSL in the standards case. The difference between the 

base-case and a standards-case INPV is an estimate of the economic impacts that implementing 

that particular TSL would have on the industry. For the Automatic Commercial Ice Maker 

industry, DOE examined the two markup scenarios described above: the preservation of gross 

margin markup scenario and the preservation of EBIT markup scenario. 

Table 12.4.1 and Table 12.4.2 provide the INPV estimates for the Automatic Commercial

Ice Makers. 

Table 12.4.1 Manufacturer Impact Analysis for Automatic Commercial Ice Makers –

Preservation of Gross Margin Markup Scenario

Units Base 

Case

Trial Standard Level

1 2 3 4 5

INPV 2013$ millions 121.6 115.0 112.3 109.5 109.3 109.8

Change in INPV 2013$ millions - (6.6) (9.3) (12.1) (12.3) (11.8)

% - (5.4) (7.7) (10.0) (10.1) (9.7)

Product 

Conversion Costs 2013$ millions - 12.3 18.1 23.8 28.1 40.3 

Capital 

Conversion Costs 2013$ millions - 0.2 0.6 1.3 2.0 3.9 

Total Conversion 

Costs 2013$ millions - 12.6 18.7 25.1 30.0 44.1 

* Values in parentheses are negative numbers.

*For tables in section 12.5, values in parenthesis indicate negative numbers 

12-33
Table 12.4.2 Manufacturer Impact Analysis for Automatic Commercial Ice Makers –

Preservation of EBIT Markup Scenario

Units Base 

Case

Trial Standard Level

1 2 3 4 5

INPV 2013$ millions 121.6 114.1 110.4 106.5 103.0 91.6

Change in INPV 2013$ millions - (7.5) (11.2) (15.1) (18.6) (30.0)

% - (6.2) (9.2) (12.5) (15.3) (24.6)

Product 

Conversion Costs 2013$ millions - 12.3 18.1 23.8 28.1 40.3 

Capital 

Conversion Costs 2013$ millions - 0.2 0.6 1.3 2.0 3.9 

Total Conversion 

Costs 2013$ millions - 12.6 18.7 25.1 30.0 44.1 

* Values in parentheses are negative numbers.

*For tables in section 12.5, values in parenthesis indicate negative numbers 

12.4.2 Impacts on Annual Cash Flow

Annual cash flows are discounted to the base year, 2015. Between 2015 and the 2018

compliance date for, cash flows are driven by the level of conversion costs and the proportion of 

these investments spent every year. After the standard announcement date (i.e., the publication 

date of the final rule), industry cash flows begin to decline as companies use their financial 

resources to prepare for the new and amended energy conservation standard. The more stringent 

the new and amended energy conservation standard, the greater the impact on industry cash 

flows in the years leading up to the compliance date, as product conversion costs lower cash 

inflows from operations and capital conversion costs increase cash outflows for capital 

expenditures. Figure 12.4.1 and Figure 12.4.2 present the annual free cash flows for the 

Automatic Commercial Ice Maker Industry.

12-34
Figure 12.4.1 Annual Industry Net Cash Flows for Automatic Commercial Ice Makers 

(Preservation of Gross Margin Markup Scenario)

Figure 12.4.2 Annual Industry Net Cash Flows for Automatic Commercial Ice Makers 

(Preservation of EBIT Markup Scenario)

12.5 IMPACTS ON SMALL AUTOMATIC COMMERCIAL ICE MAKER

MANUFACTURERS

For manufacturers of automatic commercial ice makers, the Small Business 

Administration (SBA) has set a size threshold, which defines those entities classified as “small 

businesses” for the purposes of the statute. DOE used the SBA’s small business size standards to 

($10.0)

($5.0)

$0.0

$5.0

$10.0

$15.0

$20.0

Millions $

Net Free Cash Flow Totals

Base Case TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

($10.0)

($5.0)

$0.0

$5.0

$10.0

$15.0

Millions $

Net Free Cash Flow Totals

Base Case TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

12-35
determine whether any small entities would be subject to the requirements of the rule. 65 FR 

30836, 30848 (May 15, 2000), as new and amended at 65 FR 53533, 53544 (September 5, 2000) 

and codified at 13 CFR part 121.The size standards are listed by North American Industry 

Classification System (NAICS) code and industry description and are available at 

http://www.sba.gov/sites/default/files/files/Size_Standards_Table.pdf. Commercial refrigeration 

equipment manufacturing is classified under NAICS 333415, “Air-Conditioning and Warm Air 

Heating Equipment and Commercial and Industrial Refrigeration Equipment Manufacturing”

which includes ice-making machinery manufacturing. The SBA sets a threshold of 750 

employees or less for an entity to be considered as a small business for this category.

12.5.1 Description and Estimated Number of Small Entities Regulated 

During its market survey, DOE used available public information to identify potential 

small manufacturers. DOE’s research involved industry trade association membership directories 

(including AHRI), public databases (e.g., AHRI Directory,d the SBA Databasee), individual 

company websites, and market research tools (e.g., Hoovers reportsf) to create a list of 

companies that manufacture or sell products covered by this rulemaking. DOE also asked 

stakeholders and industry representatives if they were aware of any other small manufacturers 

during manufacturer interviews and at DOE public meetings. DOE reviewed publicly available 

data and contacted select companies on its list, as necessary, to determine whether they met the 

SBA’s definition of a small business manufacturer of covered automatic commercial ice makers. 

DOE screened out companies that do not offer products covered by this rulemaking, do not meet 

the definition of a “small business,” or are foreign owned. 

DOE identified 16 manufacturers of automatic commercial ice makers. Seven of those 

are small businesses manufacturers operating in the United States. DOE contacted each of these 

companies, but only one accepted the invitation to participate in a confidential manufacturer 

impact analysis interview with DOE contractors. 

Based on a 2008 study by Koeller & Companyg

, DOE understands that the 

ACIM market is dominated by four manufacturers who produce approximately 90 percent of the 

automatic commercial ice makers for sale in the United States. The four major manufacturers 

with the largest market share are Manitowoc, Scotsman, Hoshizaki, and Ice-O-Matic. The 

remaining 12 large and small manufacturers account for ten percent of domestic sales. 

d

 "AHRI Certification Directory." AHRI Certification Directory. AHRI. (Available at: 

https://www.ahridirectory.org/ahridirectory/pages/home.aspx) (Last accessed October 10, 2011) . See 

www.ahridirectory.org/ahriDirectory/pages/home.aspx. e

 “Dynamic Small Business Search.” SBA. (Available at: See http://dsbs.sba.gov/dsbs/search/dsp_dsbs.cfm) (Last 

accessed October 12, 2011).. f

 “Hoovers | Company Information | Industry Information | Lists.” D&B (2013) (Available at: See 

http://www.hoovers.com/) (Last accessed December 12, 2012).. g Koeller, John, P.E., and Herman Hoffman, P.E. A Report on Potential Best Management Practices. Rep. The 

California Urban Water Conservation Council, n.d. Web. 19 May 2014. 

12-36

 
12.5.2 Description and Estimate of Compliance Requirements 

For the purposes of analysis, DOE assumes that the seven small domestic manufacturers 

of automatic commercial ice makers identified account for approximately 5 percent of industry 

shipments. While small business manufacturers of automatic commercial ice makers have small 

overall market share, some hold substantial market share in specific equipment classes. Several 

of these smaller firms specialize in producing industrial ice machines and the covered equipment 

they manufacture are extensions of industrial product lines that fall within the range of capacity 

covered by this rule. Others serve niche markets. Most have substantial portions of their business 

derived from equipment outside the scope of this rulemaking, but are still considered small 

businesses based on the SBA limits for number of employees. 

At the new and amended level, small business manufacturers of automatic commercial 

ice makers are expected to face negative impacts on INPV that are approximately four times as 

severe as those felt by the industry at large: a loss of 49.8 percent of INPV for small businesses 

alone as compared to a loss of 12.5 percent for the industry at large. Where conversion costs are 

driven by the number of platforms requiring redesign at a particular standard level, small 

business manufacturers may be disproportionately affected. Product conversion costs including 

the investments made to redesign existing equipment to meet new or new and amended standards 

or to develop entirely new compliant equipment, as well as industry certification costs, do not 

scale with sales volume. As small manufacturers’ investments are spread over a much lower 

volume of shipments, recovering the cost of upfront investments is proportionately more 

difficult. Additionally, smaller manufacturers typically do not have the same technical resources 

and testing capacity as larger competitors.

The product conversion investments required to comply are estimated to be over 10 times 

larger than the typical R&D expenditures for small businesses, whereas the industry as a whole is 

estimated to incur 4 times larger than typical R&D expenditures. Where the covered equipment 

from several small manufacturers are adaptations of larger platforms with capacities above the 

4,000 lb ice/24 hour threshold, it may not prove economical for them to invest in redesigning 

such a small portion of their product offering to meet standards. 

In confidential interviews, manufacturers indicated that many design options evaluated in 

the engineering analysis (e.g., higher efficiency motors and compressors) would require them to 

purchase more expensive components. In many industries, small manufacturers typically pay 

higher prices for components due to smaller purchasing volumes while their large competitors 

receive volume discounts. However, this effect is diminished for the automatic commercial ice 

maker manufacturing industry for two distinct reasons. One reason relates to the fact that the 

automatic commercial ice maker industry as a whole is a low volume industry. In confidential 

interviews, manufacturers indicated that they have little influence over their suppliers, suggesting 

the volume of their component orders is similarly insufficient to receive substantial discounts. 

The second reason relates to the fact that, for most small businesses, the equipment covered by 

this rulemaking represents only a fraction of overall business. Where small businesses are 

ordering similar components for non-covered equipment, their purchase volumes may not be as 

low as is indicated by the total unit shipments for small businesses. For these reasons, it is 

12-37
expected that any volume discount for components enjoyed by large manufacturers would not be 

substantially different from the prices paid by small business manufacturers. 

To estimate how small manufacturers would be potentially impacted, DOE developed 

specific small business inputs and scaling factors for the GRIM. These inputs were scaled from 

those used in the whole industry GRIM using information about the product portfolios of small 

businesses and the estimated market share of these businesses in each equipment class. DOE 

used this information in the GRIM to estimate the annual revenue, EBIT, R&D expense, and 

capital expenditures for a typical small manufacturer and to model the impact on INPV. DOE 

then compared these impacts to those modeled for the industry at large. The results are shown in

Table 12.5.1 and Table 12.5.2.

Table 12.5.1 Comparison of Small Business Manufacturers of Automatic Commercial Ice 

Maker INPV to that of the Industry at Large by TSL under the Preservation of Gross 

Margin Markup Scenario*

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

Industry at Large –

Impact on INPV (%) (6.2) (9.2) (12.5) (15.3) (24.6)

Small Businesses –

Impact on INPV (%) (18.3) (34.2) (48.8) (51.5) (57.2)

*Values in parentheses are negative numbers.

Table 12.5.2 Comparison of Small Business Manufacturers of Automatic Commercial Ice 

Maker INPV to that of the Industry at Large by TSL under the Preservation of EBIT 

Markup Scenario

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

Industry at Large –

Impact on INPV (%) (5.4) (7.7) (10.0) (10.1) (9.7)

Small Businesses –

Impact on INPV (%) (19.1) (35.1) (49.8) (52.6) (68.4)

*Values in parentheses are negative numbers.

12.6 OTHER IMPACTS

12.6.1 Employment

12.6.1.1 Impacts on Direct Employment

DOE used the GRIM to estimate the domestic labor expenditures and number of 

domestic production workers in the base case and at each TSL from 2015 through 2047. DOE 

used statistical data from the most recent U.S Census Bureau’s “2011 Annual Survey of 

Manufacturers (ASM)” the results of the engineering analysis, and interviews with 

manufacturers to determine the inputs necessary to calculate industry-wide labor expenditures 

and domestic employment levels. Labor expenditures related the manufacture of a product are a 

function of the labor intensity of the product, the sales volume, and an assumption that wages in 

real terms remain constant.

12-38
In the GRIM, DOE used the labor content of each product and the manufacturing 

production costs from the engineering analysis to estimate the annual labor expenditures in the 

automatic commercial ice maker industry. The total labor expenditures in the GRIM were then 

converted to domestic production employment levels by dividing production labor expenditures 

by the annual payment per production worker (production worker hours multiplied by the labor 

rate found in the U.S. Census Bureau's ASM).

The estimates of production workers in this section cover workers, including linesupervisors, who are directly involved in fabricating and assembling automatic commercial ice 

makers within an original equipment manufacturer (OEM) facility. Workers performing services 

that are closely associated with production operations, such as material handling with a forklift, 

are also included as production labor. 

The employment impacts shown in Table 12.6.1 represent the potential production 

employment changes that could result following the compliance date of new and amended 

energy conservation standards. The upper end of the employment results in Table 12.6.1

estimates the maximum increase in the number of production workers after implementation of 

new or amended energy conservation standards and it assumes that manufacturers continue to 

produce the same scope of covered products in the U.S. The lower end of employment results in 

Table 12.6.1 represent the maximum decrease to the total number of U.S. production workers in 

the industry due to manufacturers moving production outside of the U.S. While the results 

present a range of employment impacts following the compliance date of the new and amended 

energy conservation standards, the following discussion also includes a qualitative discussion of 

the likelihood of negative employment impacts at the various TSLs. Finally, the employment 

impacts shown are independent of the employment impacts from the broader U.S. economy, 

which are documented in chapter 13 of the final rule TSD.

DOE estimates that in the absence of new and amended energy conservation standards, 

there would be 389 domestic production workers involved in manufacturing automatic 

commercial ice makers in 2018. Using 2011 Census Bureau data and interviews with 

manufacturers, DOE estimates that approximately 84 percent of automatic commercial ice

makers sold in the United States are manufactured domestically. Table 12.6.1 shows the range of 

the impacts of potential new and amended energy conservation standards on U.S. production 

workers in the automatic commercial ice maker industry.

12-39
Table 12.6.1 Potential Changes in the Total Number of Domestic Automatic Commercial

Ice Maker Production Workers in 2018*

Base 

Case TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

Total Number of 

Domestic Production 

Workers in 2018 

(without changes in 

production locations)

389 391 402 414 418 444

Potential Changes in 

Domestic Production 

Workers in 2018*

- (389) to 2 (389) to 13 (389) to 25 (389) to 29 (389) to 55

* DOE presents a range of potential employment impacts. Values in parentheses are negative numbers. 

At all TSLs, most of the design options analyzed by DOE do not greatly alter the labor 

content of the final product. For example, the use of higher efficiency compressors or fan motors 

involve one-time changes to the final product but do not significantly change the amount of 

production hours required for the final assembly. One manufacturer suggested that their domestic 

production employment levels would only change if market demand contracted following higher 

overall prices. However, more than one manufacturer suggested that where they already have 

overseas manufacturing capabilities, they would consider moving additional manufacturing to 

those facilities if they felt the need to offset a significant rise in materials costs. Provided the 

changes in materials costs do not support the relocation of manufacturing facilities, DOE expects

only modest changes to domestic manufacturing employment balancing additional requirements 

for assembly labor with the effects of price elasticity. 

12.6.2 Production Capacity

According to the majority of automatic commercial ice maker manufacturers interviewed, 

new and amended energy conservation standards that require modest changes to product 

efficiency will not significantly affect manufacturers’ production capacities. Any redesign of 

automatic commercial ice makers would not change the fundamental assembly of the equipment, 

but manufacturers do anticipate some potential for additional lead time immediately following 

standards associated with changes in sourcing of higher efficiency components, which may be 

supply constrained. 

One manufacturer cited the possibility of a 3- to 6-month shutdown in the event that new 

and amended standards were set high enough to require retooling of their entire product line. 

Most of the design options being evaluated are already available on the market as product 

options. Thus, DOE believes that, short of widespread retooling, manufacturers would be able to 

maintain manufacturing capacity levels and continue to meet market demand under new and 

amended energy conservation standards.

12-40
12.6.3 Cumulative Regulatory Burden 

While any one regulation may not impose a significant burden on manufacturers, the 

combined effects of recent or impending regulations may have serious consequences for some 

manufacturers, groups of manufacturers, or an entire industry. Assessing the impact of a single 

regulation may overlook this cumulative regulatory burden. In addition to energy conservation 

standards, other regulations can significantly affect manufacturers’ financial operations. Multiple 

regulations affecting the same manufacturer can strain profits and lead companies to abandon 

product lines or markets with lower expected future returns than competing products. For these 

reasons, DOE conducts an analysis of cumulative regulatory burden as part of its rulemakings 

pertaining to equipment efficiency. 

For the cumulative regulatory burden analysis, DOE looks at other regulations that could 

affect ACIM manufacturers that will take effect approximately 3 years before or after the 2018

compliance date of new and amended energy conservation standards for these products. In 

written comments, manufacturers cited Federal regulations on equipment other than automatic 

commercial ice makers that contribute to their cumulative regulatory burden. The compliance 

years and expected industry conversion costs of relevant new and amended energy conservation 

standards are indicated in Table 12.6.2.

Table 12.6.2 Compliance Dates and Expected Conversion Expenses of Federal Energy 

Conservation Standards Affecting Automatic Commercial Ice Maker Manufacturers

Federal Energy Conservation 

Standards

Approximate Compliance 

Date

Estimated Total Industry 

Conversion Expense

Commercial Refrigeration 

Equipment

79 FR 17725 (March 28, 2014)

2017 $184.0M (2012$)

Walk-in Coolers and Freezers

79 FR 32049 (June 3, 2014) 2017 $33.6.0M (2012$)

Miscellaneous Refrigeration 

Equipment* TBD TBD

The final rule for this energy conservation standard has not been published. The compliance date and analysis of 

conversion costs have not been finalized at this time.

12.6.3.1 Other Regulations That Could Impact Automatic Commercial Ice Maker

Manufacturers

While the cumulative regulatory burden focuses on the impacts on manufacturers of other 

Federal requirements, in this section DOE has described a number of other regulations below 

that could also impact the Automatic Commercial Ice Makers covered by this rulemaking.

12-41
State Energy Conservation Standards

Manufacturers indicated that California has several programs that are either already in 

place or are currently in development that affect manufacturers of Automatic Commercial Ice 

Makers. Various building, electrical, mechanical, and plumbing codes in California affect Ice 

Makers, and products are also subject to California’s laws on the Restriction on the use of certain 

Hazardous Substances (RoHS). California’s RoHS law took effect January 1, 2007 and was 

modeled after the European Union’s (EU’s) directive (described below), which bans certain 

hazardous substances from electrical and electronic equipment.

International Energy Conservation Standards

Automatic Commercial Ice Maker manufacturers that sell products outside of the United 

States are subject to several international energy conservation standards. In the EU, products are 

also subject to RoHS. This regulation bans the sale of new equipment in the EU that contains 

quantities in excess of agreed upon levels for lead, cadmium, mercury, hexavalent chromium, 

polybrominated biphenyl (PBB) and PBDE flame retardants. Waste Electrical and Electronic 

Equipment (WEEE) and the Registration, Evaluation, Authorization, and restriction of 

Chemicals (REACH) are additional regulations that create compliance costs for manufacturers 

that compete in Europe. REACH deals with chemicals and their safe use and has provisions that 

will be phased-in over 11 years, beginning June 1, 2007. The EU also sets limits for the amount 

of energy consumed by equipment when it is in standby mode and off mode. Additionally, HFCs 

are banned in refrigerants in several countries, such as Austria, Denmark, and Switzerland. 

Canada and several other foreign countries have regulations or have initiated regulations 

affecting Ice Maker manufacturers.

12.7 CONCLUSION

The following sections summarize the different impacts for the scenarios DOE believes 

are most likely to capture the range of impacts on Automatic Commercial Ice Maker

manufacturers at each TSL in the standards case. While these scenarios bound the range of the 

most plausible impacts on manufacturers, some circumstances could cause manufacturers to 

experience impacts outside this range.

12.7.1 Automatic Commercial Ice Makers

At TSL 1, DOE estimates impacts on INPV for manufacturers of automatic commercial 

ice makers to range from -$7.5 million to -$6.6 million, or a change in INPV of -6.2 percent to -

5.4 percent. At this TSL, industry free cash flow is estimated to decrease to $6.7 million, or a 

drop of 35.7 percent, compared to the base-case value of $10.4 million in the year before the 

compliance date (2017).

DOE estimates that approximately 27 percent of all batch commercial ice makers and 29 

percent of all continuous commercial ice makers on the market will require redesign to meet 

standards at TSL 1. At this TSL DOE expects capital and product conversion costs of $0.2 

million and $12.3 million, respectively. Combined, the total conversion cost is $12.5 million.

12-42
At TSL 2, DOE estimates impacts on INPV for manufacturers of automatic commercial 

ice makers to range from -$11.2 million to -$9.3 million, or a change in INPV of -9.2 percent to -

7.7 percent. At this TSL, industry free cash flow is estimated to decrease to $4.8 million, or a 

drop of 53.5 percent, compared to the base-case value of $10.4 million in the year before the 

compliance date (2017). 

DOE estimates that approximately 39 percent of all batch commercial ice makers and 41 

percent of all continuous commercial ice makers on the market will require redesign to meet 

standards at TSL 2. At this TSL, DOE expects industry capital and product conversion costs of 

$0.6 million and of $18.1 million, respectively. Combined, the total conversion cost is $18.7 

million, 48 percent higher than those incurred by industry at TSL 1.

At TSL 3, DOE estimates impacts on INPV for manufacturers of automatic commercial 

ice makers to range from -$15.1 million to -$12.1 million, or a change in INPV of -12.5 percent 

to -10.0 percent. At this TSL, industry free cash flow is estimated to decrease to $2.9 million, or 

a drop of 72.4 percent, compared to the base-case value of $10.4 million in the year before the 

compliance date (2017).

DOE estimates that approximately 51 percent of all batch commercial ice makers and 55

percent of all continuous commercial ice makers on the market will require redesign to meet 

standards at TSL 3. At this TSL, DOE expects industry capital and product conversion costs of 

$23.8 million and of $1.3 million, respectively. Combined, the total conversion cost is $25.1 

million, 34 percent higher than those incurred by industry at TSL 2.

At TSL 4, DOE estimates impacts on INPV for manufacturers of automatic commercial 

ice makers to range from -$18.6 million to -$12.3 million, or a change in INPV of -15.3 percent 

to -10.1 percent. At this TSL, industry free cash flow is estimated to decrease to $9.50.9 million, 

or a drop of 91.1 percent, compared to the base-case value of $10.4 million in the year before the 

compliance date (2017).

DOE estimates that approximately 66 percent of all batch commercial ice makers and 55 

percent of all continuous commercial ice makers on the market will require redesign to meet 

standards at TSL 4. Additionally, for four equipment classes, there is only one manufacturer with 

products that currently meet the standard. At this TSL, DOE expects industry capital and product 

conversion costs of $2.0 million and of $28.1 million, respectively. Combined, the total 

conversion cost is $30.0 million, 20 percent higher than those incurred by industry at TSL 3.

At TSL 5, DOE estimates impacts on INPV for manufacturers of automatic commercial 

ice makers to range from -$30.0 million to -$11.8 million, or a change in INPV of -24.6 percent 

to -9.7 percent. At this TSL, industry free cash flow is estimated to decrease to -$5.3 million, or a 

drop of 151.1 percent, compared to the base-case value of $10.4 million in the year before the 

compliance date (2017).

DOE estimates that approximately 84 percent of all batch commercial ice makers and 78

percent of all continuous commercial ice makers on the market will require redesign to meet 

standards at TSL 5. Additionally, for five equipment classes, there is only one manufacturer with 

12-43
products that currently meet the standard. At this TSL, DOE expects industry capital and product 

conversion costs of $3.9 million and of $40.3 million, respectively. Combined, the total 

conversion cost is $44.1 million, 47 percent higher than those incurred by industry at TSL 4. 

12-44
REFERENCES

1 Association of Home Appliance Manufacturers. Current Members. Last accessed October 2, 

2011. Available online at: www.aham.org/ht/d/Organizations/divisions/Y/pid/2344.

2 California Energy Commission. Appliance Efficiency Database. Last accessed October 2011. 

Available online at: www.appliances.energy.ca.gov/QuickSearch.aspx

12-45
APPENDIX 12A. GOVERNMENT REGULATORY IMPACT MODEL (GRIM)

OVERVIEW

TABLE OF CONTENTS

12A.1 INTRODUCTION AND PURPOSE.......................................................................... 12A-1

12A.2 MODEL DESCRIPTION ........................................................................................... 12A-1

LIST OF TABLES

Table 12A.2.1 Detailed Cash Flow Example.......................................................................... 12A-4

12A-i
APPENDIX 12A. GOVERNMENT REGULATORY IMPACT MODEL 

(GRIM) OVERVIEW

12A.1 INTRODUCTION AND PURPOSE

The purpose of the Government Regulatory Impact Model (GRIM) is to help quantify the 

impacts of energy conservation standards and other regulations on manufacturers. The basic 

mode of analysis is to estimate the change in the value of the industry or manufacturers(s) 

following a regulation or a series of regulations. The model structure also allows an analysis of 

multiple products with regulations taking effect over a period of time, and of multiple regulations 

on the same products.

Industry net present value is defined, for the purpose of this analysis, as the discounted 

sum of industry free cash flows plus a discounted terminal value. The model calculates the actual 

cash flows by year and then determines the present value of those cash flows both without an 

energy conservation standard (i.e., the base case) and under different trial standard levels (i.e., 

the standards case).

Output from the model consists of summary financial metrics, graphs of major variables, 

and, when appropriate, access to the complete cash flow calculation.

12A.2 MODEL DESCRIPTION

The basic structure of the GRIM is a standard annual cash flow analysis that uses 

manufacturer selling prices, manufacturing costs, a shipments forecast, and financial parameters 

as inputs and accepts a set of regulatory conditions as changes in costs and investments. The cash 

flow analysis is separated into two major blocks: income and cash flow. The income calculation 

determines net operating profit after taxes. The cash flow calculation converts net operating 

profit after taxes into an annual cash flow by including investment and non-cash items. Below 

are definitions of listed items on the printout of the output sheet of the GRIM.

(1) Revenues: Annual revenues – computed by multiplying products’ unit prices at each 

efficiency level by the appropriate manufacturer markup.

(2) Unit Sales: Total annual shipments for the industry were obtained from the National Impact 

Analysis Spreadsheet.

(3) Materials: The portion of COGS that includes materials.

(4) Labor: The portion of cost of goods sold (COGS) that includes direct labor, commissions, 

dismissal pay, bonuses, vacation, sick leave, social security contributions, fringe, and 

assembly labor up-time.

(5) Depreciation: The portion of overhead that includes an allowance for the total amount of 

fixed assets used to produce that one unit. Annual depreciation is computed as a percentage 

of COGS. While included in overhead, the depreciation is shown as a separate line item.

12A-1
(6) Overhead: The portion of COGS that includes indirect labor, indirect material, energy use, 

maintenance, depreciation, property taxes, and insurance related to assets. While included 

in overhead, the depreciation is shown as a separate line item.

(7) Standard SG&A: Selling, general, and administrative costs are computed as a percentage of 

Revenues (1).

(8) R&D: GRIM separately accounts for ordinary research and development (R&D) as a 

percentage of Revenues (1).

(9) Product Conversion Costs: Product conversion costs are one-time investments in research, 

development, testing, marketing, and other costs focused on making products designs 

comply with the new energy conservation standard. The GRIM allocates these costs over 

the period between the standard’s announcement and compliance dates.

(10) Stranded Assets: In the year the standard becomes effective, a one-time write-off of 

stranded assets is accounted for.

(11) Earnings Before Interest and Taxes (EBIT): Includes profits before deductions for interest 

paid and taxes.

(12) EBIT as a Percentage of Sales (EBIT/Revenues): GRIM calculates EBIT as a percentage 

of sales to compare with the industry’s average reported in financial statements.

(13) Taxes: Taxes on EBIT (12) are calculated by multiplying the tax rate contained in Major 

Assumptions by EBIT (12).

(14) Net Operating Profits After Taxes (NOPAT): Computed by subtracting Cost of Goods 

Sold ((3) to (6)), SG&A (7), R&D (8), Product Conversion Costs (9), and Taxes (13) from 

Revenues (1).

(15) NOPAT repeated: NOPAT is repeated in the Statement of Cash Flows.

(16) Depreciation repeated: Depreciation and Stranded Assets are added back in the Statement 

of Cash Flows because they are non-cash expenses.

(17) Change in Working Capital: Change in cash tied up in accounts receivable, inventory, and 

other cash investments necessary to support operations is calculated by multiplying 

working capital (as a percentage of revenues) by the change in annual revenues. 

(18) Cash Flow From Operations: Calculated by taking NOPAT (15), adding back non-cash 

items such as a Depreciation (16), and subtracting the Change in Working Capital (17).

(19) Ordinary Capital Expenditures: Ordinary investments in property, plant, and equipment to 

maintain and replace existing production assets, computed as a percentage of Revenues (1).

12A-2
(20) Capital Conversion Costs: Capital conversion costs are one-time investments in property, 

plant, and equipment to adapt or change existing production facilities so that new product 

designs can be fabricated and assembled under the new regulation. The GRIM allocates 

these costs over the period between the standard’s announcement and compliance dates.

(21) Capital Investment: Total investments in property, plant, and equipment are computed by 

adding Ordinary Capital Expenditures (19) and Capital Conversion Costs (20).

(22) Free Cash Flow: Annual cash flow from operations and investments; computed by 

subtracting Capital Investment (21) from Cash Flow from Operations (18).

(23) Terminal Value: Estimate of the continuing value of the industry after the analysis period. 

Computed by growing the Free Cash Flow at the beginning of 2047 at a constant rate in 

perpetuity.

(24) Present Value Factor: Factor used to calculate an estimate of the present value of an 

amount to be received in the future.

(25) Discounted Cash Flow: Free Cash Flows (22) multiplied by the Present Value Factor 

(24). For the end of 2046, the discounted cash flow includes the discounted Terminal Value 

(23).

(26) Industry Value thru the end of 2046: The sum of Discounted Cash Flows (25).

12A-3
Table 12A.2.1 Detailed Cash Flow Example

12A-4
CHAPTER 13. EMISSIONS IMPACT ANALYSIS

TABLE OF CONTENTS

13.1 INTRODUCTION ............................................................................................................ 13-1

13.2 AIR QUALITY REGULATIONS AND EMISSIONS IMPACTS.................................. 13-2

13.3 POWER SECTOR EMISSIONS FACTORS ................................................................... 13-3

13.4 UPSTREAM FACTORS .................................................................................................. 13-4

13.5 EMISSIONS IMPACT RESULTS................................................................................... 13-5

REFERENCES .......................................................................................................................... 13-9

LIST OF TABLES

Table 13.3.1 Power Plant Emissions Factors............................................................................. 13-4

Table 13.4.1 Electricity Upstream Emissions Factors............................................................... 13-5

Table 13.5.1 Cumulative Emissions Reduction for Potential Standards for Automatic 

Commercial Ice Makers..................................................................................... 13-5

LIST OF FIGURES

Figure 13.5.1 Automatic Commercial Ice Makers: CO2 Total Emissions Reduction ............... 13-6

Figure 13.5.2 Automatic Commercial Ice Makers: SO2 Total Emissions Reduction................ 13-6

Figure 13.5.3 Automatic Commercial Ice Makers: NOx Total Emissions Reduction............... 13-7

Figure 13.5.4 Automatic Commercial Ice Makers: Hg Total Emissions Reduction ................. 13-7

Figure 13.5.5 Automatic Commercial Ice Makers: N2O Total Emissions Reduction............... 13-8

Figure 13.5.6 Automatic Commercial Ice Makers: CH4 Total Emissions Reduction ............... 13-8

13-i
CHAPTER 13. EMISSIONS IMPACT ANALYSIS

13.1 INTRODUCTION

The emissions analysis consists of two components. The first component estimates the 

effect of potential energy conservation standards on power sector emissions of carbon dioxide 

(CO2), nitrogen oxides (NOx), sulfur dioxide (SO2), and mercury (Hg). The second component 

estimates the impacts of a potential standard on emissions of two additional greenhouse gases, 

methane (CH4) and nitrous oxide (N2O), as well as the reductions to emissions of all species due 

to “upstream” activities in the fuel production chain. These upstream activities comprise 

extraction, processing, and transporting fuels to the site of combustion. The associated emissions 

are referred to as upstream emissions. Together, these emissions account for the full-fuel-cycle 

(FFC), in accordance with the U.S. Department of Energy’s (DOE’s) FFC Statement of Policy.

76 FR 51282 (Aug. 18, 2011). 

The analysis of most power sector emissions uses marginal emissions intensity factors 

derived from runs of DOE’s National Energy Modeling System – Building Technologies 

(NEMS-BT) model, described in Chapter 15. DOE used the version of NEMS based on the 

Annual Energy Outlook 2014 (AEO2014).

1 Each annual version of NEMS incorporates the 

projected impacts of existing air quality regulations on emissions. AEO2014 generally represents 

current Federal and state legislation and final implementation regulations in place as of the end 

of October 2013. Where applicable, site emissions of CO2 and NOx are estimated using 

emissions intensity factors from a publication of the Environmental Protection Agency (EPA).

2

Combustion emissions of CH4 and N2O (from power plants and from upstream 

combustion) are estimated using emissions intensity factors published by the EPA, GHG 

Emissions Factors Hub.a

The FFC upstream emissions are estimated based on the methodology developed by 

Coughlin (2013).3 The upstream emissions include both emissions from fuel combustion during 

extraction, processing, and transportation of fuel, and “fugitive” emissions (direct leakage to the 

atmosphere) of CH4 and CO2.

The emissions intensity factors are expressed in terms of physical units per megawatthours (MWh) or million British thermal units (MMBtu) of site energy savings. Total emissions 

reductions are estimated using the energy savings calculated in the national impact analysis 

(chapter 10).

For CH4 and N2O, DOE also presents results in terms of units of carbon dioxide 

equivalent (CO2e). Gases are converted to CO2e by multiplying the physical units by the gas' 

global warming potential (GWP) over a 100-year time horizon. Based on the Fifth Assessment 

a http://www.epa.gov/climateleadership/inventory/ghg-emissions.html

13-1

 
Report of the Intergovernmental Panel on Climate Change,4 DOE used GWP values of 28 for 

CH4 and 265 for N2O.

b

13.2 AIR QUALITY REGULATIONS AND EMISSIONS IMPACTS

SO2 emissions from affected electric generating units (EGUs) are subject to nationwide 

and regional emissions cap and trading programs. Title IV of the Clean Air Act sets an annual 

emissions cap on SO2 for affected EGUs in the 48 contiguous states and the District of Columbia 

(D.C.). SO2 emissions from 28 eastern states and D.C. were also limited under the Clean Air 

Interstate Rule (CAIR), which created an allowance-based trading program that operates along 

with the Title IV program in those states and D.C. 70 FR 25162 (May 12, 2005). CAIR was 

remanded to EPA by the U.S. Court of Appeals for the District of Columbia Circuit (D.C. 

Circuit) but parts of it remained in effect. In 2011 EPA issued a replacement for CAIR, the 

Cross-State Air Pollution Rule (CSAPR). 76 FR 48208 (Aug. 8, 2011). On August 21, 2012, the 

D.C. Circuit issued a decision to vacate CSAPR.c

 The court ordered EPA to continue 

administering CAIR. The emissions factors used for this analysis, which are based on AEO2014, 

assume that CAIR remains a binding regulation through 2040.d

The attainment of emissions caps is typically flexible among affected EGUs and is 

enforced through the use of emissions allowances and tradable permits. Under existing EPA 

regulations, any excess SO2 emissions allowances resulting from the lower electricity demand 

caused by the imposition of an efficiency standard could be used to permit offsetting increases in 

SO2 emissions by any regulated EGU. In past rulemakings, DOE recognized that there was 

uncertainty about the effects of efficiency standards on SO2 emissions covered by the existing 

cap-and-trade system, but it concluded that no reductions in power sector emissions would occur 

for SO2 as a result of standards.

Beginning in 2016, however, SO2 emissions will fall as a result of the Mercury and Air 

Toxics Standards (MATS) for power plants. 77 FR 9304 (Feb. 16, 2012).e In the final MATS 

rule, EPA established a standard for hydrogen chloride as a surrogate for acid gas hazardous air 

b The values are without inclusion of climate-carbon feedbacks in response to emissions of the indicated non-CO2 

gases.

c See EME Homer City Generation, LP v. EPA, 696 F.3d 7, 38 (D.C. Cir. 2012).

d On April 29, 2014, the U.S. Supreme Court reversed the judgment of the D.C. Circuit. The case will now return to 

the D.C. Circuit for further proceedings consistent with the Supreme Court's opinion. The Supreme Court held in 

part that EPA's methodology for quantifying emissions that must be eliminated in certain states due to their impacts 

in other downwind states was based on a permissible, workable, and equitable interpretation of the Clean Air Act 

provision that provides statutory authority for CSAPR. See EPA v. EME Homer City Generation, No 12-1182, slip 

op. at 32 (U.S. April 29, 2014). Because DOE is using emissions factors based onAEO2014, it assumes that CAIR, 

not CSAPR, is the regulation in force. The difference between CAIR and CSAPR is not relevant for the purpose of 

DOE's analysis of SO2 emissions. e On July 20, 2012, EPA announced a partial stay, for a limited duration, of the effectiveness of national new source 

emission standards for hazardous air pollutants from coal- and oil-fired electric utility steam generating units. 

www.reginfo.gov/public/do/eAgendaViewRule?pubId=201110&RIN=2060-AP52

13-2

 
pollutants (HAP), and also established a standard for SO2 (a non-HAP acid gas) as an alternative 

equivalent surrogate standard for acid gas HAP. The same controls are used to reduce HAP and 

non-HAP acid gas; thus, SO2 emissions will be reduced as a result of the control technologies 

installed on coal-fired power plants to comply with the MATS requirements for acid gas. 

AEO2014 assumes that, in order to continue operating, coal plants must have either flue gas 

desulfurization or dry sorbent injection systems installed by 2016. Both technologies, which are 

used to reduce acid gas emissions, also reduce SO2 emissions. Under the MATS, NEMS shows a 

reduction in SO2 emissions when electricity demand decreases (e.g., as a result of energy 

efficiency standards). Emissions will be far below the cap established by CAIR, so it is unlikely 

that excess SO2 emissions allowances resulting from the lower electricity demand would be 

needed or used to permit offsetting increases in SO2 emissions by any regulated EGU. Therefore, 

DOE believes that efficiency standards will reduce SO2 emissions in 2016 and beyond.

CAIR established a cap on NOx emissions in 28 eastern states and the District of 

Columbia. Energy conservation standards are expected to have little effect on NOx emissions in 

those states covered by CSAPR because excess NOx emissions allowances resulting from the 

lower electricity demand could be used to permit offsetting increases in NOx emissions.

However, standards would be expected to reduce NOx emissions in the states not affected by 

CAIR, so DOE estimated NOx emissions reductions from potential standards for those states.

The MATS limit mercury emissions from power plants, but they do not include 

emissions caps and, as such, DOE’s energy conservation standards would likely reduce Hg 

emissions. DOE estimated mercury emissions reductions using the NEMS-BT based on 

AEO2014, which incorporates the MATS.

13.3 POWER SECTOR EMISSIONS FACTORS 

The analysis of power sector emissions uses marginal emissions intensity factors derived 

from runs of DOE’s NEMS-BT model, using the version updated to the Annual Energy Outlook 

2014 (AEO2014). To model the impact of a standard, DOE inputs a reduction to annual energy 

demand for the corresponding end use in the appropriate start year. The NEMS-BT model is run 

with the decremented energy demand to determine the modified build-out of capacity, fuel use 

and power sector emissions. A marginal emissions intensity factor is defined by dividing the 

reduction in the total emissions of a given pollutant by the reduction in total generation (in 

billion kWh). DOE uses the site energy savings multiplied by a transmission and distribution 

(T&D) loss factor to estimate the reduction in generation for each trial standard level (TSL).

Details on the approach used may be found in Coughlin (2013).3

Table 13.3.1 presents the average power plant emissions factors for selected years. These

power plant emissions factors are derived from the emissions factors of the plant types used to 

supply electricity to buildings. DOE used the commercial refrigeration end use load shape. The 

average factors for each year take into account the projected shares of each of the sources in total 

electricity generation. 

13-3
The power plant emissions factor for NOx is an average for the entire U.S. The marginal 

calculation based on the NEMS-BT model accounts for the fact that NOx emissions are capped 

in some states. 

Table 13.3.1 Power Plant Emissions Factors

Unit 2018 2020 2025 2030 2035 2040

CO2 kg/MWh 734 650 586 536 489 734

SO2 g/MWh 735 574 482 405 362 735

NOx g/MWh 585 488 426 375 340 585

Hg g/MWh 0.002 0.002 0.001 0.001 0.001 0.002

N2O g/MWh 7.2 7.1 6.9 6.6 6.4 7.2

CH4 g/MWh 50 49 48 46 45 50

13.4 UPSTREAM FACTORS

The upstream emissions accounting uses the same approach as the upstream energy 

accounting described in appendix 10D. See also Coughlin (2013).3 When demand for a particular 

fuel is reduced, there is a corresponding reduction in the emissions from combustion of that fuel 

at either the building site or the power plant. The associated reduction in energy use for 

upstream activities leads to further reductions in emissions. These upstream emissions are 

defined to include the combustion emissions from the fuel used upstream, the fugitive emissions 

associated with the fuel used upstream, and the fugitive emissions associated with the fuel used 

on site. 

Fugitive emissions of CO2 occur during oil and gas production, but are small relative to 

combustion emissions. They comprise about 2.5 percent of total CO2 emissions for natural gas 

and 1.7 percent for petroleum fuels. Fugitive emissions of methane occur during oil, gas and coal 

production. Combustion emissions of CH4 are very small, while fugitive emissions (particularly 

for gas production) may be relatively large. Hence, fugitive emissions make up over 99 percent

of total methane emissions for natural gas, about 95 percent for coal, and 93 percent for 

petroleum fuels. 

Upstream emissions factors account for both fugitive emissions and combustion 

emissions in extraction, processing, and transport of primary fuels. Fugitive emissions factors for 

methane from coal mining and natural gas production were estimated based on a review of recent 

studies compiled by Burnham (2011).5 This review includes estimates of the difference between 

fugitive emissions factors for conventional production of natural vs. unconventional (shale or 

tight gas). These estimates rely in turn on data gathered by EPA under new greenhouse gas 

(GHG) reporting requirements for the petroleum and natural gas industries.6,7 As more data are 

made available, DOE will continue to update these estimated emissions factors. 

For ease of application in its analysis, DOE developed all of the emissions factors using 

site (point of use) energy savings in the denominator. Table 13.4.1 presents the electricity 

upstream emissions factors for selected years. The caps that apply to power sector NOx

emissions do not apply to upstream combustion sources.

13-4
Table 13.4.1 Electricity Upstream Emissions Factors

Unit 2018 2020 2025 2030 2035 2040

CO2 kg/MWh 29.1 29.4 29.7 29.9 29.8 29.1

SO2 g/MWh 5.0 5.1 4.9 4.7 4.6 5.0

NOx g/MWh 368 375 382 387 387 368

Hg g/MWh 0.00001 0.00001 0.00001 0.00001 0.00001 0.00001

N2O g/MWh 0.252 0.247 0.241 0.234 0.228 0.252

CH4 g/MWh 2,149 2,195 2,216 2,248 2,255 2,149

13.5 EMISSIONS IMPACT RESULTS

Table 13.5.1 presents the estimated cumulative emissions reductions for the lifetime of 

equipment sold in 2018-2047 for each TSL.

Table 13.5.1 Cumulative Emissions Reduction for Potential Standards for Automatic 

Commercial Ice Makers

Trial Standard Level

1 2 3 4 5

Power Sector Emissions

 CO2 (million metric tons) 4.68 7.87 10.38 13.25 18.62

 NOx (thousand tons) 3.71 6.23 8.22 10.50 14.75

 Hg (tons) 0.01 0.02 0.03 0.04 0.05

 N2O (thousand tons) 0.06 0.11 0.14 0.18 0.25

 CH4 (thousand tons) 0.44 0.73 0.97 1.24 1.74

 SO2 (thousand tons) 4.13 6.95 9.17 11.70 16.45

Upstream Emissions

 CO2 (million metric tons) 0.25 0.42 0.56 0.72 1.00

 NOx (thousand tons) 3.59 6.03 7.96 10.17 14.29

 Hg (tons) 0.000 0.000 0.000 0.000 0.000

 N2O (thousand tons) 0.00 0.00 0.00 0.01 0.01

 CH4 (thousand tons) 20.91 35.15 46.40 59.23 83.24

 SO2 (thousand tons) 0.04 0.08 0.10 0.13 0.18

Total Emissions

 CO2 (million metric tons) 4.93 8.29 10.94 13.97 19.63

 NOx (thousand tons) 7.30 12.26 16.19 20.67 29.04

 Hg (tons) 0.01 0.02 0.03 0.04 0.05

 N2O (thousand tons) 0.06 0.11 0.14 0.18 0.26

 CH4 (thousand tons) 21.35 35.89 47.37 60.47 84.97

 SO2 (thousand tons) 4.18 7.02 9.27 11.83 16.62

Figure 13.5.1 through Figure 13.5.6 show the annual reductions for total emissions for 

each type of emission from each TSL. The reductions reflect the lifetime impacts of equipment 

sold in 2018-2047.

13-5
Figure 13.5.1 Automatic Commercial Ice Makers: CO2 Total Emissions Reduction

Figure 13.5.2 Automatic Commercial Ice Makers: SO2 Total Emissions Reduction

0.0

100.0

200.0

300.0

400.0

500.0

600.0

700.0

800.0

201820232028203320382043204820532058

kt

CO2 Emissions Reduction

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

0.0

100.0

200.0

300.0

400.0

500.0

600.0

700.0

800.0

201820232028203320382043204820532058

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SO2 Emissions Reduction

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

13-6
Figure 13.5.3 Automatic Commercial Ice Makers: NOx Total Emissions Reduction

Figure 13.5.4 Automatic Commercial Ice Makers: Hg Total Emissions Reduction

0.0

200.0

400.0

600.0

800.0

1000.0

1200.0

201820232028203320382043204820532058

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NOx Emissions Reduction

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

0.0000

0.0003

0.0006

0.0009

0.0012

0.0015

0.0018

0.0021

0.0024

201820232028203320382043204820532058

ton

Hg Emissions Reduction

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

13-7
Figure 13.5.5 Automatic Commercial Ice Makers: N2O Total Emissions Reduction

Figure 13.5.6 Automatic Commercial Ice Makers: CH4 Total Emissions Reduction

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

9.0

10.0

201820232028203320382043204820532058

ton

N2OEmissions Reduction

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

0.0

500.0

1000.0

1500.0

2000.0

2500.0

3000.0

3500.0

201820232028203320382043204820532058

ton

CH4 Emissions Reduction

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

13-8
REFERENCES

1. U.S. Department of Energy—Energy Information Administration. Annual Energy 

Outlook 2013 with Projections to 2040. 2013. Washington, DC. 

<http://www.eia.gov/forecasts/aeo/pdf/0383(2013).pdf>

2. U.S. Environmental Protection Agency. Compilation of Air Pollutant Emission Factors, 

AP-42, Fifth Edition, Volume I: Stationary Point and Area Sources. 1998. 

<http://www.epa.gov/ttn/chief/ap42/index.html>

3. Coughlin, K. Projections of Full-Fuel-Cycle Energy and Emissions Metrics. 2013. 

Lawrence Berkeley National Laboratory: Berkeley, CA. Report No. LBNL-6025E.

4. IPCC, 2013: Climate Change 2013: The Physical Science Basis. Contribution of Working

Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate 

Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. 

Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, 

Cambridge, United Kingdom and New York, NY, USA. Chapter 8.

5. Burnham, A., J. Han, C. E. Clark, M. Wang, J. B. Dunn, I. Palou-Rivera. Life-Cycle 

Greenhouse Gas Emissions of Shale Gas, Natural Gas, Coal, and Petroleum.

Environmental Science & Technology. 2011. 46(2): pp. 619–627.

6. U.S. Environmental Protection Agency. Fugitive Emissions Reporting from the 

Petroleum and Natural Gas Industry. 2009. U.S. Environmental Protection Agency. 

Washington, DC.

7. U.S. Environmental Protection Agency. Oil and Natural Gas Sector: Standards of 

Performance for Crude Oil and Natural Gas Production, Transmission, and Distribution, 

Background Supplemental Technical Support Document for the Final New Source 

Performance Standards. 2012. U.S. Environmental Protection Agency. Washington, DC.

13-9
CHAPTER 14. MONETIZATION OF EMISSION REDUCTIONS BENEFITS 

TABLE OF CONTENTS

14.1 INTRODUCTION ............................................................................................................ 14-1

14.2 MONETIZING CARBON DIOXIDE EMISSIONS........................................................ 14-1

14.2.1 Social Cost of Carbon ........................................................................................... 14-1

14.2.2 Monetizing Carbon Dioxide Emissions................................................................ 14-1

14.2.3 Current Approach and Key Assumptions............................................................. 14-2

14.3 VALUATION OF OTHER EMISSIONS REDUCTIONS .............................................. 14-5

14.4 RESULTS ......................................................................................................................... 14-5

REFERENCES .......................................................................................................................... 14-8

LIST OF TABLES

Table 14.2.1 Annual SCC Values from 2010 Interagency Report, 2010–2050 (in 2007$ 

per metric ton CO2)............................................................................................ 14-4

Table 14.2.2 Annual SCC Values from 2013 Interagency Update, 2010–2050 (in 2007$ 

per metric ton CO2)............................................................................................ 14-4

Table 14.4.1 Estimates of Global Present Value of CO2 Emissions Reduction under 

Automatic Commercial Ice Makers Trial Standard Levels ............................... 14-6

Table 14.4.2 Estimates of Domestic Present Value of CO2 Emissions Reduction under 

Automatic Commercial Ice Makers Trial Standard Levels ............................... 14-6

Table 14.4.3 Estimates of Present Value of NOx Emissions Reduction under Automatic 

Commercial Ice Makers Trial Standard Levels................................................. 14-7

14-i
CHAPTER 14. MONETIZATION OF EMISSION REDUCTIONS BENEFITS 

14.1 INTRODUCTION

As part of its assessment of energy conservation standards for automatic commercial ice 

makers, the U.S. Department of Energy (DOE) estimated the monetary benefits likely to result 

from the reduced emissions of carbon dioxide (CO2) and nitrogen oxides (NOx) that are expected 

to result from each of the trial standard levels (TSLs) considered. This chapter summarizes the 

basis for the monetary values used for each of these emissions and presents the benefits estimates 

considered. 

14.2 MONETIZING CARBON DIOXIDE EMISSIONS

14.2.1 Social Cost of Carbon

The social cost of carbon (SCC) is an estimate of the monetized damages associated with 

an incremental increase in carbon emissions in a given year. It is intended to include (but is not 

limited to) changes in net agricultural productivity, human health, property damages from 

increased flood risk, and the value of ecosystem services. Estimates of the SCC are provided in 

dollars per metric ton of carbon dioxide. A domestic SCC value is meant to reflect the value of 

damages in the United States resulting from a unit change in carbon dioxide emissions, while a 

global SCC value is meant to reflect the value of damages worldwide.

Under section 1(b) of Executive Order 12866, agencies must, to the extent permitted by 

law, “assess both the costs and the benefits of the intended regulation and, recognizing that some 

costs and benefits are difficult to quantify, propose or adopt a regulation only upon a reasoned 

determination that the benefits of the intended regulation justify its costs.” The purpose of the 

SCC estimates presented here is to allow agencies to incorporate the monetized social benefits of 

reducing CO2 emissions into cost-benefit analyses of regulatory actions that have small, or 

“marginal,” impacts on cumulative global emissions. The estimates are presented with an 

acknowledgement of the many uncertainties involved and with a clear understanding that they 

should be updated over time to reflect increasing knowledge of the science and economics of 

climate impacts.

As part of the interagency process that developed these SCC estimates, technical experts 

from numerous agencies met on a regular basis to explore the technical literature in relevant 

fields, discuss key model inputs and assumptions, and consider public comments. The main 

objective of this process was to develop a range of SCC values using a defensible set of input 

assumptions grounded in the existing scientific and economic literatures. In this way, key 

uncertainties and model differences transparently and consistently inform the range of SCC 

estimates used in the rulemaking process.

14.2.2 Monetizing Carbon Dioxide Emissions

When attempting to assess the incremental economic impacts of carbon dioxide 

emissions, the analyst faces a number of serious challenges. A report from the National Research 

Council

1 points out that any assessment will suffer from uncertainty, speculation, and lack of 

14-1
information about (1) future emissions of greenhouse gases, (2) the effects of past and future 

emissions on the climate system, (3) the impact of changes in climate on the physical and 

biological environment, and (4) the translation of these environmental impacts into economic 

damages. As a result, any effort to quantify and monetize the harms associated with climate 

change will raise serious questions of science, economics, and ethics and should be viewed as 

provisional. 

Despite the serious limits of both quantification and monetization, SCC estimates can be 

useful in estimating the social benefits of reducing carbon dioxide emissions. Most Federal 

regulatory actions can be expected to have marginal impacts on global emissions. For such 

policies, the agency can estimate the benefits from reduced (or costs from increased) emissions 

in any future year by multiplying the change in emissions in that year by the SCC value 

appropriate for that year. The net present value of the benefits can then be calculated by 

multiplying each of these future benefits by an appropriate discount factor and summing across 

all affected years. This approach assumes that the marginal damages from increased emissions 

are constant for small departures from the baseline emissions path, an approximation that is 

reasonable for policies that have effects on emissions that are small relative to cumulative global 

carbon dioxide emissions.

In 2009, an interagency process was initiated to offer a preliminary assessment of how 

best to quantify the benefits from reducing carbon dioxide emissions. To ensure consistency in 

how benefits are evaluated across agencies, the Administration sought to develop a transparent 

and defensible method, specifically designed for the rulemaking process, to quantify avoided 

climate change damages from reduced CO2 emissions. The interagency group did not undertake 

any original analysis. Instead, it combined SCC estimates from the existing literature to use as 

interim values until a more comprehensive analysis could be conducted. The outcome of the 

preliminary assessment by the interagency group was a set of five interim values: global SCC 

estimates for 2007 (in 2006 dollars) of $55, $33, $19, $10, and $5 per ton of CO2.

2 These interim 

values represented the first sustained interagency effort within the U.S. government to develop 

an SCC for use in regulatory analysis. The results of this preliminary effort were presented in 

several proposed and final rules.

14.2.3 Current Approach and Key Assumptions

After the release of the interim values, the interagency group reconvened on a regular 

basis to generate improved SCC estimates, which were considered for this final rule. 

Specifically, the group considered public comments and further explored the technical literature 

in relevant fields. The interagency group relied on three integrated assessment models (IAMs) 

commonly used to estimate the SCC: the FUND, DICE, and PAGE models.a These models are 

frequently cited in the peer-reviewed literature and were used in the last assessment of the 

Intergovernmental Panel on Climate Change. Each model was given equal weight in the SCC 

values that were developed. 

a The models are described in appendix 14A of this technical support document (TSD).

14-2

 
Each model takes a slightly different approach to model how changes in emissions result 

in changes in economic damages. A key objective of the interagency process was to enable a 

consistent exploration of the three models while respecting the different approaches to 

quantifying damages taken by the key modelers in the field. An extensive review of the literature 

was conducted to select three sets of input parameters for these models: (1) climate sensitivity,

(2) socio-economic and emissions trajectories, and (3) discount rates. A probability distribution 

for climate sensitivity was specified as an input into all three models. In addition, the interagency 

group used a range of scenarios for the socio-economic parameters and a range of values for the 

discount rate. All other model features were left unchanged, relying on the model developers’ 

best estimates and judgments.

The interagency group selected four SCC values for use in regulatory analyses.b (The 

2010 report is reproduced in appendix 14A.) Three values are based on the average SCC from 

three integrated assessment models, at discount rates of 2.5, 3, and 5 percent. The fourth value, 

which represents the 95th percentile SCC estimate across all three models at a 3-percent discount 

rate, is included to represent higher-than-expected impacts from temperature change further out 

in the tails of the SCC distribution. The values grow in real terms over time, as depicted in

Table 14.2.1. Additionally, the interagency group determined that a range of values from 7 

percent to 23 percent should be used to adjust the global SCC to calculate domestic effects,c

although preference is given to consideration of the global benefits of reducing CO2 emissions.

The SCC values used for this final rule were generated using the most recent versions of 

the three integrated assessment models that have been published in the peer-reviewed literature, 

as described in the 2013 update3 from the interagency working group (revised November 2013).

d

Table 14.2.2 shows the updated sets of SCC estimates in 5-year increments from 2010 to 2050. 

Appendix 14B provides the full set of SCC estimates, as well as the 2013 report from the 

interagency group. The central value that emerges is the average SCC across models at the 3-

percent discount rate. However, for purposes of capturing the uncertainties involved in 

regulatory impact analysis, the interagency group emphasizes the importance of including all 

four sets of SCC values.

b Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866. Interagency Working Group 

on Social Cost of Carbon, United States Government, February 2010. 

http://www.whitehouse.gov/sites/default/files/omb/inforeg/for-agencies/Social-Cost-of-Carbon-for-RIA.pdf

c It is recognized that this calculation for domestic values is approximate, provisional, and highly speculative. There 

is no a priori reason why domestic benefits should be a constant fraction of net global damages over time.

d Technical Update of the Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866. 

Interagency Working Group on Social Cost of Carbon, United States Government. May 2013; revised November 

2013. http://www.whitehouse.gov/sites/default/files/omb/assets/inforeg/technical-update-social-cost-of-carbon-forregulator-impact-analysis.pdf.

14-3

 
Table 14.2.1 Annual SCC Values from 2010 Interagency Report, 2010–2050 (in 2007$ per 

metric ton CO2)

Year

Discount Rate 

%

5 3 2.5 3

Average Average Average 95th Percentile

2010 4.7 21.4 35.1 64.9

2015 5.7 23.8 38.4 72.8

2020 6.8 26.3 41.7 80.7

2025 8.2 29.6 45.9 90.4

2030 9.7 32.8 50.0 100.0

2035 11.2 36.0 54.2 109.7

2040 12.7 39.2 58.4 119.3

2045 14.2 42.1 61.7 127.8

2050 15.7 44.9 65.0 136.2

Table 14.2.2 Annual SCC Values from 2013 Interagency Update, 2010–2050 (in 2007$ per 

metric ton CO2)

Year

Discount Rate 

%

5 3 2.5 3

Average Average Average 95th Percentile

2010 11 32 51 89

2015 11 37 57 109

2020 12 43 64 128

2025 14 47 69 143

2030 16 52 75 159

2035 19 56 80 175

2040 21 61 86 191

2045 24 66 92 206

2050 26 71 97 220

It is important to recognize that a number of key uncertainties remain, and that current 

SCC estimates should be treated as provisional and revisable since they will evolve with 

improved scientific and economic understanding. The interagency group also recognizes that the 

existing models are imperfect and incomplete. The National Research Council report mentioned 

above points out that there is tension between the goal of producing quantified estimates of the 

economic damages from an incremental ton of carbon and the limits of existing efforts to model 

these effects. There are a number of concerns and problems that should be addressed by the 

research community, including research programs housed in many of the agencies participating 

in the interagency process to estimate the SCC. The interagency group intends to periodically 

review and reconsider estimates of the SCC used for cost-benefit analyses to reflect increasing 

knowledge of the science and economics of climate impacts, as well as improvements in 

modeling.

In summary, in considering the potential global benefits resulting from reduced CO2

emissions, DOE used the values from the 2013 interagency report, which is reprinted in appendix 

14B of this TSD, escalated to 2013$ using the gross domestic product (GDP) price deflator. For 

each of the four cases specified, the values used for emissions in 2015 were $12.0, $40.5, $62.4, 

14-4
and $119 per metric ton avoided. DOE derived values after 2050 using the relevant growth rates 

for the 2040–2050 period in the interagency update.

DOE multiplied the CO2 emissions reduction estimated for each year by the SCC value 

for that year in each of the four cases. To calculate a present value of the stream of monetary 

values, DOE discounted the values in each of the four cases using the specific discount rate that 

had been used to obtain the SCC values in each case.

14.3 VALUATION OF OTHER EMISSIONS REDUCTIONS

DOE considered the potential monetary benefit of reduced NOx emissions from the TSLs 

it considered. As noted in chapter 13, new or amended energy conservation standards would 

reduce NOx emissions in those 22 states that are not affected by caps. DOE estimated the 

monetized value of NOx emissions reductions resulting from each of the TSLs considered based 

on environmental damage estimates found in the relevant scientific literature. Available 

estimates suggest a very wide range of monetary values, ranging from $476 to $4,893 per ton in 

2013$).

4 In accordance with OMB guidance, DOE calculated a range of monetary benefits using 

each of the economic values for NOX and real discount rates of 3 percent and 7 percent.

5

DOE is still evaluating appropriate values to use to monetize avoided SO2 and Hg 

emissions. It did not monetize these emissions for this final rule.

14.4 RESULTS

Table 14.4.1 presents the global values of CO2 emissions reductions for each considered 

TSL. DOE calculated domestic values as a range from 7 percent to 23 percent of the global 

values, and these results are presented in Table 14.4.2.

14-5
Table 14.4.1 Estimates of Global Present Value of CO2 Emissions Reduction under 

Automatic Commercial Ice Makers Trial Standard Levels

TSL

SCC Case*

5% discount 

rate, average

3% discount 

rate, average

2.5% discount 

rate, average

3% discount rate, 

95th percentile

million 2013$

Primary Energy Emissions

1 34.5 154.3 243.8 476.2

2 57.9 259.4 409.9 800.5

3 76.4 342.3 541.0 1,056.6

4 97.6 437.0 690.6 1,348.9

5 137.1 614.1 970.5 1,895.5

Upstream Emissions

1 1.8 8.2 13.0 25.4

2 3.0 13.8 21.9 42.7

3 4.0 18.2 28.8 56.3

4 5.1 23.3 36.8 71.9

5 7.2 32.7 51.8 101.0

Total Emissions

1 36.3 162.5 256.8 501.6

2 61.0 273.2 431.7 843.1

3 80.5 360.6 569.8 1,112.9

4 102.7 460.3 727.5 1,420.8

5 144.3 646.8 1,022.3 1,996.5

* For each of the four cases, the corresponding SCC value for emissions in 2015 is $12.0, $40.5, 

$62.4, and $119.0 per metric ton (2013$).

Table 14.4.2 Estimates of Domestic Present Value of CO2 Emissions Reduction under 

Automatic Commercial Ice Makers Trial Standard Levels

TSL

SCC Case*

5% discount 

rate, average

3% discount 

rate, average

2.5% discount 

rate, average

3% discount rate, 

95th percentile

million 2013$

Primary Energy Emissions

1 2.4 to 7.9 10.8 to 35.5 17.1 to 56.1 33.3 to 109.5

2 4.1 to 13.3 18.2 to 59.7 28.7 to 94.3 56.0 to 184.1

3 5.4 to 17.6 24.0 to 78.7 37.9 to 124.4 74.0 to 243.0

4 6.8 to 22.4 30.6 to 100.5 48.3 to 158.8 94.4 to 310.2

5 9.6 to 31.5 43.0 to 141.3 67.9 to 223.2 132.7 to 436.0

Upstream Emissions

1 0.1 to 0.4 0.6 to 1.9 0.9 to 3.0 1.8 to 5.8

2 0.2 to 0.7 1.0 to 3.2 1.5 to 5.0 3.0 to 9.8

3 0.3 to 0.9 1.3 to 4.2 2.0 to 6.6 3.9 to 13.0

4 0.4 to 1.2 1.6 to 5.3 2.6 to 8.5 5.0 to 16.5

5 0.5 to 1.7 2.3 to 7.5 3.6 to 11.9 7.1 to 23.2

Total Emissions

1 2.5 to 8.3 11.4 to 37.4 18.0 to 59.1 35.1 to 115.4

2 4.3 to 14.0 19.1 to 62.8 30.2 to 99.3 59.0 to 193.9

3 5.6 to 18.5 25.2 to 82.9 39.9 to 131.1 77.9 to 256.0

4 7.2 to 23.6 32.2 to 105.9 50.9 to 167.3 99.5 to 326.8

5 10.1 to 33.2 45.3 to 148.8 71.6 to 235.1 139.8 to 459.2

* For each of the four cases, the corresponding SCC value for emissions in 2015 is $12.0, $40.5, 

$62.4, and $119.0 per metric ton (2013$).

14-6
Table 14.4.3 presents the present value of cumulative NOx emissions reductions for each 

TSL, calculated using the average dollar-per-ton values and 3-percent and 7-percent discount 

rates.

Table 14.4.3 Estimates of Present Value of NOx Emissions Reduction under Automatic 

Commercial Ice Makers Trial Standard Levels

TSL 3% discount rate 7% discount rate

million 2013$

Power Sector Emissions

1 5.6 2.9

2 9.4 4.9

3 12.4 6.5

4 15.8 8.2

5 22.2 11.6

Upstream Emissions

1 5.2 2.5

2 8.7 4.3

3 11.4 5.6

4 14.6 7.2

5 20.5 10.1

Total Emissions

1 10.7 5.4

2 18.0 9.2

3 23.8 12.1

4 30.4 15.4

5 42.7 21.7

14-7
REFERENCES

1. National Research Council. Hidden Costs of Energy: Unpriced Consequences of Energy 

Production and Use. 2009. National Academies Press. Washington, D.C. 

<www.nap.edu/catalog.php?record_id=12794>

2. Interagency Working Group on Social Cost of Carbon. Social Cost of Carbon for 

Regulatory Impact Analysis under Executive Order 12866. February 2010. United States 

Government. <www.whitehouse.gov/sites/default/files/omb/inforeg/for-agencies/SocialCost-of-Carbon-for-RIA.pdf>

3. Interagency Working Group on Social Cost of Carbon. Technical Update of the Social Cost 

of Carbon for Regulatory Impact Analysis Under Executive Order 12866. November 2013. 

United States Government.

<http://www.whitehouse.gov/sites/default/files/omb/assets/inforeg/technical-update-socialcost-of-carbon-for-regulator-impact-analysis.pdf>

4. U.S. Office of Management and Budget (OMB). 2006 Report to Congress on the Costs and 

Benefits of Federal Regulations and Unfunded Mandates on State, Local, and Tribal 

Entities. 2006. Washington, D.C. 

<www.whitehouse.gov/sites/default/files/omb/assets/omb/inforeg/2006_cb/2006_cb_final_r

eport.pdf>

5. U.S. Office of Management and Budget (OMB). Circular A-4: Regulatory Analysis. 2003.

(Posted September 17, 2003) (Last accessed April 2013.) 

<http://www.whitehouse.gov/sites/default/files/omb/assets/regulatory_matters_pdf/a-4.pdf>

14-8
APPENDIX 14A. SOCIAL COST OF CARBON FOR REGULATORY IMPACT 

ANALYSIS UNDER EXECUTIVE ORDER 12866

TABLE OF CONTENTS

14A.1 EXECUTIVE SUMMARY ........................................................................................ 14A-1

14A.2 MONETIZING CARBON DIOXIDE EMISSIONS.................................................. 14A-2

14A.3 SOCIAL COST OF CARBON VALUES USED IN PAST REGULATORY ANALYSES

..................................................................................................................................... 14A-4

14A.4 APPROACH AND KEY ASSUMPTIONS ............................................................... 14A-5

14A.4.1 Integrated Assessment Models ................................................................... 14A-5

14A.4.2 Global versus Domestic Measures of SCC............................................... 14A-11

14A.4.3 Valuing Non-CO2 Emissions.................................................................... 14A-12

14A.4.4 Equilibrium Climate Sensitivity ............................................................... 14A-13

14A.4.5 Socio-Economic and Emissions Trajectories............................................ 14A-16

14A.4.6 Discount Rate............................................................................................ 14A-18

14A.5 REVISED SCC ESTIMATES .................................................................................. 14A-25

14A.6 LIMITATIONS OF THE ANALYSIS ..................................................................... 14A-29

14A.7 A FURTHER DISCUSSION OF CATASTROPHIC IMPACTS AND DAMAGE 

FUNCTIONS ............................................................................................................ 14A-32

14A.7.1 Extrapolation of Climate Damages to High Levels of Warming.............. 14A-32

14A.7.2 Failure to Incorporate Inter-Sectoral and Inter-Regional Interactions...... 14A-33

14A.7.3 Imperfect Substitutability of Environmental Amenities........................... 14A-34

14A.8 CONCLUSION......................................................................................................... 14A-34

14A.9 REFERENCES ......................................................................................................... 14A-36

14A.9.1 ANNEX..................................................................................................... 14A-42

14A.9.2 Other (Non-CO2) Gases............................................................................ 14A-43

14A.9.3 Extrapolating Emissions Projections to 2300 ........................................... 14A-45

LIST OF TABLES

Table 14A.1.1 Social Cost of CO2, 2010–2050 (2007$) ........................................................ 14A-2

Table 14A.4.1 Summary Statistics for Four Calibrated Climate Sensitivity Distributions.. 14A-14

Table 14A.4.2 Socioeconomic and Emissions Projections from Select EMF-22 Reference 

Scenarios....................................................................................................... 14A-17

Table 14A.5.1 Disaggregated Social Cost of CO2 Values by Model, Socio-Economic 

Trajectory, and Discount Rate for 2010 (2007$).......................................... 14A-27

Table 14A.5.2 Social Cost of CO2, 2010–2050 (2007$) ...................................................... 14A-29

Table 14A.5.3 Changes in the Average Annual Growth Rates of SCC Estimates between 

2010 and 2050............................................................................................... 14A-29

Table 14A.7.1 Probabilities of Various Tipping Points from Expert Elicitation.................. 14A-33

Table 14A.9.1 Annual SCC Values: 2010–2050 (2007$) .................................................... 14A-42

Table 14A.9.2 2010 Global SCC Estimates at 2.5-Percent Discount Rate (2007$/ton CO2)14A-50

Table 14A.9.3 2010 Global SCC Estimates at 3-Percent Discount Rate (2007$/ton CO2).. 14A-50

Table 14A.9.4 2010 Global SCC Estimates at 5-Percent Discount Rate (2007$/ton CO2).. 14A-51

14A-i
Table 14A.9.5 Additional Summary Statistics of 2010 Global SCC Estimates................... 14A-52

LIST OF FIGURES

Figure 14A.4.1 Annual Consumption Loss as a Fraction of Global GDP in 2100 Due to an 

Increase in Annual Global Temperature in the DICE, FUND, and PAGE 

Models........................................................................................................... 14A-10

Figure 14A.4.2 Annual Consumption Loss for Lower Temperature Changes in DICE, FUND, 

and PAGE ..................................................................................................... 14A-10

Figure 14A.4.3 Estimates of the Probability Density Function for Equilibrium Climate 

Sensitivity ..................................................................................................... 14A-15

Figure 14A.5.1 Level of Global GDP Across EMF Scenarios............................................. 14A-28

Figure 14A.9.1 Sulphur Dioxide Emission Scenarios.......................................................... 14A-45

Figure 14A.9.2 Global Population, 2000–2300 (post-2100 extrapolations assume the 

population growth rate changes linearly to reach a zero growth rate by 2200)14A-47

Figure 14A.9.3 World GDP, 2000-2300 (post-2100 extrapolations assume GDP per capita 

growth declines linearly, reaching zero in 2300).......................................... 14A-47

Figure 14A.9.4 Global Fossil and Industrial CO2 Emissions, 2000-2300 (post-2100 

extrapolations assume growth rate of CO2 intensity (CO2/GDP) over 2090–

2100 is maintained through 2300) ................................................................ 14A-48

Figure 14A.9.5 Global Net Land Use CO2 Emissions, 2000–2300 (post-2100 extrapolations 

assume emissions decline linearly, reaching zero in 2200).......................... 14A-48

Figure 14A.9.6 Global Non-CO2 Radiative Forcing, 2000–2300 (post-2100 extrapolations 

assume constant non-CO2 radiative forcing after 2100)............................... 14A-49

Figure 14A.9.7 Global CO2 Intensity (fossil & industrial CO2 emissions/GDP), 2000–2300 

(post-2100 extrapolations assume decline in CO2/GDP growth rate over 2090–

2100 is maintained through 2300) ................................................................ 14A-49

Figure 14A.9.8 Histogram of Global SCC Estimates in 2010 (2007$/ton CO2), by Discount 

Rate* ............................................................................................................. 14A-51

14A-ii
APPENDIX 14A. SOCIAL COST OF CARBON FOR REGULATORY IMPACT 

ANALYSIS UNDER EXECUTIVE ORDER 12866

Prepared by

Interagency Working Group on Social Cost of Carbon, United States Government

With participation by

Council of Economic Advisers 

Council on Environmental Quality 

Department of Agriculture

Department of Commerce

Department of Energy

Department of Transportation

Environmental Protection Agency

National Economic Council

Office of Energy and Climate Change

Office of Management and Budget

Office of Science and Technology Policy 

Department of the Treasury

14A.1 EXECUTIVE SUMMARY

Under Executive Order (E.O.) 12866, agencies are required, to the extent permitted by 

law, “to assess both the costs and the benefits of the intended regulation and, recognizing that 

some costs and benefits are difficult to quantify, propose or adopt a regulation only upon a 

reasoned determination that the benefits of the intended regulation justify its costs.” The purpose 

of the “social cost of carbon” (SCC) estimates presented here is to allow agencies to incorporate 

the social benefits of reducing carbon dioxide (CO2) emissions into cost-benefit analyses of 

regulatory actions that have small, or “marginal,” impacts on cumulative global emissions. The 

estimates are presented with an acknowledgement of the many uncertainties involved and with a 

clear understanding that they should be updated over time to reflect increasing knowledge of the 

science and economics of climate impacts.

The SCC is an estimate of the monetized damages associated with an incremental 

increase in carbon emissions in a given year. It is intended to include but is not limited to

changes in net agricultural productivity, human health, property damages from increased flood 

risk, and the value of ecosystem services due to climate change.

This document presents a summary of the interagency process that developed these SCC 

estimates. Technical experts from numerous agencies met on a regular basis to consider public 

comments, explore the technical literature in relevant fields, and discuss key model inputs and 

assumptions. The main objective of this process was to develop a range of SCC values using a 

defensible set of input assumptions grounded in the existing scientific and economic literatures. 

In this way, key uncertainties and model differences transparently and consistently inform the 

range of SCC estimates used in the rulemaking process.

14A-1
The interagency group selected four SCC values for use in regulatory analyses (Table 

14A.1.1. Three values are based on the average SCC from three integrated assessment models, at 

discount rates of 2.5, 3, and 5 percent. The fourth value, which represents the 95th percentile SCC 

estimate across all three models at a 3-percent discount rate, is included to represent higher-thanexpected impacts from temperature change further out in the tails of the SCC distribution.

Table 14A.1.1 Social Cost of CO2, 2010–2050 (2007$)

Year Discount Rate

%

5 3 2.5 3

Avg Avg Avg 95th

2010 4.7 21.4 35.1 64.9

2015 5.7 23.8 38.4 72.8

2020 6.8 26.3 41.7 80.7

2025 8.2 29.6 45.9 90.4

2030 9.7 32.8 50.0 100.0

2035 11.2 36.0 54.2 109.7

2040 12.7 39.2 58.4 119.3

2045 14.2 42.1 61.7 127.8

2050 15.7 44.9 65.0 136.2

14A.2 MONETIZING CARBON DIOXIDE EMISSIONS

The SCC is an estimate of the monetized damages associated with an incremental 

increase in carbon emissions in a given year. It is intended to include but is not limited to 

changes in net agricultural productivity, human health, property damages from increased flood 

risk, and the value of ecosystem services. We report estimates of the social cost of carbon in 

dollars per metric ton of CO2 throughout this document.a

When attempting to assess the incremental economic impacts of CO2 emissions, the 

analyst faces a number of serious challenges. A recent report from the National Academies of 

Science (NRC 2009) points out that any assessment will suffer from uncertainty, speculation, 

and lack of information about (1) future emissions of greenhouse gases; (2) the effects of past 

and future emissions on the climate system; (3) the impact of changes in climate on the physical 

and biological environment; and (4) the translation of these environmental impacts into 

economic damages. As a result, any effort to quantify and monetize the harms associated with 

climate change will raise serious questions of science, economics, and ethics and should be 

viewed as provisional.

Despite the serious limits of both quantification and monetization, SCC estimates can be 

useful in estimating the social benefits of reducing CO2 emissions. Under E.O. 12866, agencies 

are required, to the extent permitted by law, “to assess both the costs and the benefits of the 

intended regulation and, recognizing that some costs and benefits are difficult to quantify, 

propose or adopt a regulation only upon a reasoned determination that the benefits of the 

a In this document, we present all values of the SCC as the cost per metric ton of CO2 emissions. Alternatively, one 

could report the SCC as the cost per metric ton of carbon emissions. The multiplier for translating between mass of 

CO2 and the mass of carbon is 3.67 (the molecular weight of CO2 divided by the molecular weight of carbon = 44/12 

= 3.67). 

14A-2

 
intended regulation justify its costs.” The purpose of the SCC estimates presented here is to make 

it possible for agencies to incorporate the social benefits from reducing CO2 emissions into costbenefit analyses of regulatory actions that have small or “marginal” impacts on cumulative 

global emissions. Most Federal regulatory actions can be expected to have marginal impacts on 

global emissions. 

For such policies, the benefits from reduced (or costs from increased) emissions in any 

future year can be estimated by multiplying the change in emissions in that year by the SCC 

value appropriate for that year. The net present value of the benefits can then be calculated by 

multiplying each of these future benefits by an appropriate discount factor and summing across 

all affected years. This approach assumes that the marginal damages from increased emissions 

are constant for small departures from the baseline emissions path, an approximation that is 

reasonable for policies that have effects on emissions that are small relative to cumulative global 

CO2 emissions. For policies that have a large (non-marginal) impact on global cumulative 

emissions, there is a separate question of whether the SCC is an appropriate tool for calculating 

the benefits of reduced emissions; we do not attempt to answer that question here.

An interagency group convened on a regular basis to consider public comments, explore 

the technical literature in relevant fields, and discuss key inputs and assumptions in order to 

generate SCC estimates. Agencies that actively participated in the interagency process include 

the Environmental Protection Agency, and the Departments of Agriculture, Commerce, Energy, 

Transportation, and Treasury. This process was convened by the Council of Economic Advisers 

and the Office of Management and Budget, with active participation and regular input from the 

Council on Environmental Quality, National Economic Council, Office of Energy and Climate 

Change, and Office of Science and Technology Policy. The main objective of this process was to 

develop a range of SCC values using a defensible set of input assumptions that are grounded in 

the existing literature. In this way, key uncertainties and model differences can more 

transparently and consistently inform the range of SCC estimates used in the rulemaking process.

The interagency group selected four SCC estimates for use in regulatory analyses. For 

2010, these estimates are $4.7, $21.4, $35.1, and $64.9 (2007$). The first three estimates are 

based on the average SCC across models and socio-economic and emissions scenarios at the 5-, 

3-, and 2.5-percent discount rates, respectively. The fourth value is included to represent the 

higher-than-expected impacts from temperature change further out in the tails of the SCC 

distribution. For this purpose, we use the SCC value for the 95th percentile at a 3-percent 

discount rate. The central value is the average SCC across models at the 3-percent discount rate. 

For purposes of capturing the uncertainties involved in regulatory impact analysis, we emphasize 

the importance and value of considering the full range. These SCC estimates also grow over 

time. For instance, the central value increases to $24 per ton of CO2 in 2015 and $26 per ton of 

CO2 in 2020. See Appendix A for the full range of annual SCC estimates from 2010 to 2050.

It is important to emphasize that the interagency process is committed to updating these 

estimates as the science and economic understanding of climate change and its impacts on 

society improves over time. Specifically, we have set a preliminary goal of revisiting the SCC 

values within 2 years or at such time as substantially updated models become available, and to 

continue to support research in this area. In the meantime, we will continue to explore the issues 

14A-3
raised in this document and consider public comments as part of the ongoing interagency 

process.

14A.3 SOCIAL COST OF CARBON VALUES USED IN PAST REGULATORY 

ANALYSES

To date, economic analyses for Federal regulations have used a wide range of values to 

estimate the benefits associated with reducing CO2 emissions. In the final model year 2011 

CAFE rule, the Department of Transportation (DOT) used both a “domestic” SCC value of 

$2 per ton of CO2 and a “global” SCC value of $33 per ton of CO2 for 2007 emission reductions 

(2007$), increasing both values at 2.4 percent per year. It also included a sensitivity analysis at 

$80 per ton of CO2. A domestic SCC value is meant to reflect the value of damages in the United 

States resulting from a unit change in CO2 emissions, while a global SCC value is meant to 

reflect the value of damages worldwide.

A 2008 regulation proposed by DOT assumed a domestic SCC value of $7 per ton CO2

(in 2006 dollars) for 2011 emission reductions (with a range of $0–$14 for sensitivity analysis), 

also increasing at 2.4 percent per year. A regulation finalized by DOE in October of 2008 used a 

domestic SCC range of $0 to $20 per ton CO2 for 2007 emission reductions (2007$). In addition, 

the 2008 Advance Notice of Proposed Rulemaking for Greenhouse Gases identified what it 

described as “very preliminary” SCC estimates subject to revision. EPA’s global mean values 

were $68 and $40 per ton CO2 for discount rates of approximately 2 percent and 3 percent, 

respectively (2006$ for 2007 emissions).

In 2009, an interagency process was initiated to offer a preliminary assessment of how 

best to quantify the benefits from reducing CO2 emissions. To ensure consistency in how 

benefits are evaluated across agencies, the Administration sought to develop a transparent and 

defensible method, specifically designed for the rulemaking process, to quantify avoided climate 

change damages from reduced CO2 emissions. The interagency group did not undertake any 

original analysis. Instead, it combined SCC estimates from the existing literature to use as 

interim values until a more comprehensive analysis could be conducted. 

The outcome of the preliminary assessment by the interagency group was a set of five 

interim values: global SCC estimates for 2007 (in 2006 dollars) of $55, $33, $19, $10, and $5 per 

ton of CO2. The $33 and $5 values represented model-weighted means of the published estimates 

produced from the most recently available versions of three integrated assessment models—

DICE, PAGE, and FUND—at approximately 3- and 5-percent discount rates. The $55 and $10 

values were derived by adjusting the published estimates for uncertainty in the discount rate 

(using factors developed by Newell and Pizer (2003)) at 3- and 5-percent discount rates, 

respectively. The $19 value was chosen as a central value between the $5 and $33 per ton 

estimates. All of these values were assumed to increase at 3 percent annually to represent growth 

in incremental damages over time as the magnitude of climate change increases.

These interim values represent the first sustained interagency effort within the U.S. 

government to develop an SCC for use in regulatory analysis. The results of this preliminary 

effort were presented in several proposed and final rules and were offered for public comment in 

14A-4
connection with proposed rules, including the joint EPA-DOT fuel economy and CO2 tailpipe 

emission proposed rules.

14A.4 APPROACH AND KEY ASSUMPTIONS

Since the release of the interim values, interagency group has reconvened on a regular 

basis to generate improved SCC estimates. Specifically, the group has considered public 

comments and further explored the technical literature in relevant fields. This section details the 

several choices and assumptions that underlie the resulting estimates of the SCC. 

It is important to recognize that a number of key uncertainties remain, and that current 

SCC estimates should be treated as provisional and revisable since they will evolve with 

improved scientific and economic understanding. The interagency group also recognizes that the 

existing models are imperfect and incomplete. The National Academy of Science (2009) points 

out that there is tension between the goal of producing quantified estimates of the economic 

damages from an incremental ton of carbon and the limits of existing efforts to model these 

effects. Throughout this document, we highlight a number of concerns and problems that should 

be addressed by the research community, including research programs housed in many of the 

agencies participating in the interagency process to estimate the SCC. 

The U.S. Government will periodically review and reconsider estimates of the SCC used 

for cost-benefit analyses to reflect increasing knowledge of the science and economics of climate 

impacts, as well as improvements in modeling. In this context, statements recognizing the 

limitations of the analysis and calling for further research take on exceptional significance. The

interagency group offers the new SCC values with all due humility about the uncertainties 

embedded in them and with a sincere promise to continue work to improve them.

14A.4.1 Integrated Assessment Models 

We rely on three integrated assessment models (IAMs) commonly used to estimate the 

SCC: the FUND, DICE, and PAGE models.b These models are frequently cited in the peerreviewed literature and used in the IPCC assessment. Each model is given equal weight in the 

SCC values developed through this process, bearing in mind their different limitations (discussed 

below).

These models are useful because they combine climate processes, economic growth, and 

feedbacks between the climate and the global economy into a single modeling framework. At the 

same time, they gain this advantage at the expense of a more detailed representation of the 

underlying climatic and economic systems. DICE, PAGE, and FUND all take stylized, reducedform approaches (see NRC 2009 for a more detailed discussion; see Nordhaus 2008 on the 

possible advantages of this approach). Other IAMs may better reflect the complexity of the 

b The DICE (Dynamic Integrated Climate and Economy) model by William Nordhaus evolved from a series of 

energy models and was first presented in 1990 (Nordhaus and Boyer 2000, Nordhaus 2008). The PAGE (Policy 

Analysis of the Greenhouse Effect) model was developed by Chris Hope in 1991 for use by European decisionmakers in assessing the marginal impact of carbon emissions (Hope 2006, Hope 2008). The FUND (Climate 

Framework for Uncertainty, Negotiation, and Distribution) model, developed by Richard Tol in the early 1990s, 

originally to study international capital transfers in climate policy. is now widely used to study climate impacts (e.g., 

Tol 2002a, Tol 2002b, Anthoff et al. 2009, Tol 2009).

14A-5

 
science in their modeling frameworks but do not link physical impacts to economic damages.

There is currently a limited amount of research linking climate impacts to economic damages, 

which makes this exercise even more difficult. Underlying the three IAMs selected for this 

exercise are a number of simplifying assumptions and judgments reflecting the various modelers’ 

best attempts to synthesize the available scientific and economic research characterizing these 

relationships.

The three IAMs translate emissions into changes in atmospheric greenhouse 

concentrations, atmospheric concentrations into changes in temperature, and changes in 

temperature into economic damages. The emissions projections used in the models are based on 

specified socio-economic (gross domestic product (GDP) gross domestic product (GDP)and 

population) pathways. These emissions are translated into concentrations using the carbon cycle 

built into each model, and concentrations are translated into warming based on each model’s 

simplified representation of the climate and a key parameter, climate sensitivity. Each model 

uses a different approach to translate warming into damages. Finally, transforming the stream of 

economic damages over time into a single value requires judgments about how to discount them.

Each model takes a slightly different approach to model how changes in emissions result 

in changes in economic damages. In PAGE, for example, the consumption-equivalent damages 

in each period are calculated as a fraction of GDP, depending on the temperature in that period 

relative to the pre-industrial average temperature in each region. In FUND, damages in each 

period also depend on the rate of temperature change from the prior period. In DICE, 

temperature affects both consumption and investment. We describe each model in greater detail 

here. In a later section, we discuss key gaps in how the models account for various scientific and 

economic processes (e.g., the probability of catastrophe, and the ability to adapt to climate 

change and the physical changes it causes).

The parameters and assumptions embedded in the three models vary widely. A key 

objective of the interagency process was to enable a consistent exploration of the three models 

while respecting the different approaches to quantifying damages taken by the key modelers in 

the field. An extensive review of the literature was conducted to select three sets of input 

parameters for these models: climate sensitivity, socio-economic and emissions trajectories, and 

discount rates. A probability distribution for climate sensitivity was specified as an input into all 

three models. In addition, the interagency group used a range of scenarios for the socio-economic 

parameters and a range of values for the discount rate. All other model features were left 

unchanged, relying on the model developers’ best estimates and judgments. In DICE, these 

parameters are handled deterministically and represented by fixed constants; in PAGE, most 

parameters are represented by probability distributions. FUND was also run in a mode in which 

parameters were treated probabilistically.

14A-6
The sensitivity of the results to other aspects of the models (e.g., the carbon cycle or 

damage function) is also important to explore in the context of future revisions to the SCC but 

has not been incorporated into these estimates. Areas for future research are highlighted at the 

end of this document.

14A.4.1.1 The DICE Model

The DICE model is an optimal growth model based on a global production function with 

an extra stock variable (atmospheric CO2 concentrations). Emission reductions are treated as 

analogous to investment in “natural capital.” By investing in natural capital today through 

reductions in emissions—implying reduced consumption—harmful effects of climate change can 

be avoided and future consumption thereby increased. 

For purposes of estimating the SCC, CO2 emissions are a function of global GDP and the 

carbon intensity of economic output, with the latter declining over time due to technological 

progress. The DICE damage function links global average temperature to the overall impact on 

the world economy. It varies quadratically with temperature change to capture the more rapid 

increase in damages expected to occur under more extreme climate change, and is calibrated to 

include the effects of warming on the production of market and nonmarket goods and services. It 

incorporates impacts on agriculture, coastal areas (due to sea level rise), “other vulnerable 

market sectors” (based primarily on changes in energy use), human health (based on climaterelated diseases, such as malaria and dengue fever, and pollution), non-market amenities (based 

on outdoor recreation), and human settlements and ecosystems. The DICE damage function also 

includes the expected value of damages associated with low probability, high impact 

“catastrophic” climate change. This last component is calibrated based on a survey of experts 

(Nordhaus 1994). The expected value of these impacts is then added to the other market and nonmarket impacts mentioned above.

No structural components of the DICE model represent adaptation explicitly, though it is 

included implicitly through the choice of studies used to calibrate the aggregate damage function.

For example, its agricultural impact estimates assume that farmers can adjust land use decisions 

in response to changing climate conditions, and its health impact estimates assume 

improvements in healthcare over time. In addition, the small impacts on forestry, water systems, 

construction, fisheries, and outdoor recreation imply optimistic and costless adaptation in these 

sectors (Nordhaus and Boyer, 2000; Warren et al. 2006). Costs of resettlement due to sea level 

rise are incorporated into damage estimates, but their magnitude is not clearly reported.

Mastrandrea’s (2009) review concludes that “in general, DICE assumes very effective 

adaptation, and largely ignores adaptation costs.”

14A-7
Note that the damage function in DICE has a somewhat different meaning from the 

damage functions in FUND and PAGE. Because GDP is endogenous in DICE and because 

damages in a given year reduce investment in that year, damages propagate forward in time and 

reduce GDP in future years. In contrast, GDP is exogenous in FUND and PAGE, so damages in 

any given year do not propagate forward.c

14A.4.1.2 The PAGE Model

PAGE2002 (version 1.4epm) treats GDP growth as exogenous. It divides impacts into 

economic, non-economic, and catastrophic categories and calculates these impacts separately for 

eight geographic regions. Damages in each region are expressed as a fraction of output, where 

the fraction lost depends on the temperature change in each region. Damages are expressed as 

power functions of temperature change. The exponents of the damage function are the same in 

all regions but are treated as uncertain, with values ranging from 1 to 3 (instead of being fixed at 

2 as in DICE). 

PAGE2002 includes the consequences of catastrophic events in a separate damage subfunction. Unlike DICE, PAGE2002 models these events probabilistically. The probability of a 

“discontinuity” (i.e., a catastrophic event) is assumed to increase with temperature above a 

specified threshold. The threshold temperature, the rate at which the probability of experiencing

a discontinuity increases above the threshold, and the magnitude of the resulting catastrophe are 

all modeled probabilistically.

Adaptation is explicitly included in PAGE. Impacts are assumed to occur for temperature 

increases above some tolerable level (2 °C for developed countries and 0 °C for developing 

countries for economic impacts, and 0 °C for all regions for non-economic impacts), but 

adaptation is assumed to reduce these impacts. Default values in PAGE2002 assume that the 

developed countries can ultimately eliminate up to 90 percent of all economic impacts beyond 

the tolerable 2 °C increase and that developing countries can eventually eliminate 50 percent of 

their economic impacts. All regions are assumed to be able to mitigate 25 percent of the noneconomic impacts through adaptation (Hope 2006).

14A.4.1.3 The FUND Model

Like PAGE, the FUND model treats GDP growth as exogenous. It includes separately 

calibrated damage functions for eight market and nonmarket sectors: agriculture, forestry, water, 

energy (based on heating and cooling demand), sea level rise (based on the value of land lost and 

the cost of protection), ecosystems, human health (diarrhea, vector-borne diseases, and 

cardiovascular and respiratory mortality), and extreme weather. Each impact sector has a 

different functional form, and is calculated separately for sixteen geographic regions. In some 

c Using the default assumptions in DICE 2007, this effect generates an approximately 25 percent increase in the 

SCC relative to damages calculated by fixing GDP. In DICE2007, the time path of GDP is endogenous. Specifically, 

the path of GDP depends on the rate of saving and level of abatement in each period chosen by the optimizing 

representative agent in the model. We made two modifications to DICE to make it consistent with EMF GDP 

trajectories (see next section): we assumed a fixed rate of savings of 20%, and we re- calibrated the exogenous path 

of total factor productivity so that DICE would produce GDP projections in the absence of warming that exactly 

matched the EMF scenarios.

14A-8

 
impact sectors, the fraction of output lost or gained due to climate change depends not only on 

the absolute temperature change but also on the rate of temperature change and level of regional 

income.d In the forestry and agricultural sectors, economic damages also depend on CO2

concentrations.

Tol (2009) discusses impacts not included in FUND, noting that many are likely to have a 

relatively small effect on damage estimates (both positive and negative). However, he 

characterizes several omitted impacts as “big unknowns:” for instance, extreme climate 

scenarios, biodiversity loss, and effects on economic development and political violence. With 

regard to potentially catastrophic events, he notes, “Exactly what would cause these sorts of 

changes or what effects they would have are not well-understood, although the chance of any one 

of them happening seems low. But they do have the potential to happen relatively quickly, and if 

they did, the costs could be substantial. Only a few studies of climate change have examined 

these issues.”

Adaptation is included both implicitly and explicitly in FUND. Explicit adaptation is seen 

in the agriculture and sea level rise sectors. Implicit adaptation is included in sectors such as 

energy and human health, where wealthier populations are assumed to be less vulnerable to 

climate impacts. For example, the damages to agriculture are the sum of three effects: (1) those 

due to the rate of temperature change (damages are always positive); (2) those due to the level of 

temperature change (damages can be positive or negative depending on region and temperature); 

and (3) those from CO2 fertilization (damages are generally negative but diminishing to zero).

Adaptation is incorporated into FUND by allowing damages to be smaller if climate 

change happens more slowly. The combined effect of CO2 fertilization in the agricultural sector, 

positive impacts to some regions from higher temperatures, and sufficiently slow increases in 

temperature across these sectors can result in negative economic damages from climate change.

14A.4.1.4 Damage Functions

To generate revised SCC values, we rely on the IAM modelers’ current best judgments of 

how to represent the effects of climate change (represented by the increase in global-average 

surface temperature) on the consumption-equivalent value of both market and non-market goods 

(represented as a fraction of global GDP). We recognize that these representations are 

incomplete and highly uncertain. Given the paucity of data linking the physical impacts to 

economic damages, we were not able to identify a better way to translate changes in climate into 

net economic damages, short of launching our own research program.

The damage functions for the three IAMs are presented in Figure 14A.4.1 and Figure 

14A.4.2, using the modeler’s default scenarios and mean input assumptions. There are significant 

differences between the three models both at lower (Figure 14A.4.1) and higher (Figure 14A.4.2) 

increases in global-average temperature. 

d In the deterministic version of FUND, the majority of damages are attributable to increased air conditioning 

demand, while reduced cold stress in Europe, North America, and Central and East Asia results in health benefits in 

those regions at low to moderate levels of warming (Warren et al. 2006).

14A-9

 
Figure 14A.4.1 Annual Consumption Loss as a Fraction of Global GDP in 2100 Due to an 

Increase in Annual Global Temperature in the DICE, FUND, and PAGE Modelse

Figure 14A.4.2 Annual Consumption Loss for Lower Temperature Changes in DICE, 

FUND, and PAGE

e The x-axis represents increases in annual, rather than equilibrium, temperature, while the y-axis represents the 

annual stream of benefits as a share of global GDP. Each specific combination of climate sensitivity, socioeconomic, and emissions parameters will produce a different realization of damages for each IAM. The damage 

functions represented in Figure 14A.4.1 and Figure 14A.4.2are the outcome of default assumptions. For instance, 

under alternate assumptions, the damages from FUND may cross from negative to positive at less than or greater 

than 3 °C.

14A-10

 
The lack of agreement among the models at lower temperature increases is underscored 

by the fact that the damages from FUND are well below the 5th percentile estimated by PAGE, 

while the damages estimated by DICE are roughly equal to the 95th percentile estimated by 

PAGE. This is significant because at higher discount rates we expect that a greater proportion of 

the SCC value is due to damages in years with lower temperature increases. For example, when 

the discount rate is 2.5 percent, about 45 percent of the 2010 SCC value in DICE is due to 

damages that occur in years when the temperature is less than or equal to 3 °C. This increases to 

approximately 55 percent and 80 percent at discount rates of 3 and 5 percent, respectively.

These differences underscore the need for a thorough review of damage functions—in 

particular, how the models incorporate adaptation, technological change, and catastrophic 

damages. Gaps in the literature make modifying these aspects of the models challenging, which 

highlights the need for additional research. As knowledge improves, the Federal government is 

committed to exploring how these (and other) models can be modified to incorporate more 

accurate estimates of damages.

14A.4.2 Global versus Domestic Measures of SCC

Because of the distinctive nature of the climate change problem, we center our current 

attention on a global measure of SCC. This approach is the same as that taken for the interim 

values, but it otherwise represents a departure from past practices, which tended to put greater 

emphasis on a domestic measure of SCC (limited to impacts of climate change experienced 

within U.S. borders). As a matter of law, consideration of both global and domestic values is 

generally permissible; the relevant statutory provisions are usually ambiguous and allow 

selection of either measure.f

14A.4.2.1 Global SCC

Under current OMB guidance contained in Circular A-4, analysis of economically 

significant proposed and final regulations from the domestic perspective is required, while 

analysis from the international perspective is optional. However, the climate change problem is 

highly unusual in at least two respects. First, it involves a global externality: emissions of most 

greenhouse gases contribute to damages around the world even when they are emitted in the 

United States. Consequently, to address the global nature of the problem, the SCC must 

incorporate the full (global) damages caused by GHG emissions. Second, climate change 

presents a problem that the United States alone cannot solve. Even if the United States were to 

reduce its greenhouse gas emissions to zero, that step would be far from enough to avoid 

substantial climate change. Other countries would also need to take action to reduce emissions if 

significant changes in the global climate are to be avoided. Emphasizing the need for a global 

solution to a global problem, the United States has been actively involved in seeking 

international agreements to reduce emissions and in encouraging other nations, including 

emerging major economies, to take significant steps to reduce emissions. When these 

f It is true that Federal statutes are presumed not to have extraterritorial effect, in part to ensure that the laws of the 

United States respect the interests of foreign sovereigns. But use of a global measure for the SCC does not give 

extraterritorial effect to federal law and hence does not intrude on such interests.

14A-11

 
considerations are taken as a whole, the interagency group concluded that a global measure of 

the benefits from reducing U.S. emissions is preferable. 

When quantifying the damages associated with a change in emissions, a number of 

analysts (e.g., Anthoff et al. 2009a) employ “equity weighting” to aggregate changes in 

consumption across regions. This weighting takes into account the relative reductions in wealth 

in different regions of the world. A per-capita loss of $500 in GDP, for instance, is weighted 

more heavily in a country with a per-capita GDP of $2,000 than in one with a per-capita GDP of 

$40,000. The main argument for this approach is that a loss of $500 in a poor country causes a 

greater reduction in utility or welfare than does the same loss in a wealthy nation.

Notwithstanding the theoretical claims on behalf of equity weighting, the interagency group 

concluded that this approach would not be appropriate for estimating a SCC value used in 

domestic regulatory analysis.g For this reason, the group concluded that using the global (rather 

than domestic) value, without equity weighting, is the appropriate approach.

14A.4.2.2 Domestic SCC

As an empirical matter, the development of a domestic SCC is greatly complicated by the 

relatively few region- or country-specific estimates of the SCC in the literature. One potential 

source of estimates comes from the FUND model. The resulting estimates suggest that the ratio 

of domestic to global benefits of emission reductions varies with key parameter assumptions. For 

example, with a 2.5- or 3-percent discount rate, the U.S. benefit is about 7–10 percent of the 

global benefit, on average, across the scenarios analyzed. Alternatively, if the fraction of GDP 

lost due to climate change is assumed to be similar across countries, the domestic benefit would 

be proportional to the U.S. share of global GDP, which is currently about 23 percent.h

On the basis of this evidence, the interagency workgroup determined that a range of 

values from 7 to 23 percent should be used to adjust the global SCC to calculate domestic 

effects. Reported domestic values should use this range. It is recognized that these values are 

approximate, provisional, and highly speculative. There is no a priori reason why domestic 

benefits should be a constant fraction of net global damages over time. Further, FUND does not 

account for how damages in other regions could affect the United States (e.g., global migration, 

economic and political destabilization). If more accurate methods for calculating the domestic 

SCC become available, the Federal government will examine these to determine whether to 

update its approach.

14A.4.3 Valuing Non-CO2 Emissions

While CO2 is the most prevalent greenhouse gas emitted into the atmosphere, the U.S. 

included five other greenhouse gases in its recent endangerment finding: methane, nitrous oxide, 

g It is plausible that a loss of $X inflicts more serious harm on a poor nation than on a wealthy one, but development 

of the appropriate “equity weight” is challenging. Emissions reductions also impose costs, and hence a full account 

would have to consider that a given cost of emissions reductions imposes a greater utility or welfare loss on a poor 

nation than on a wealthy one. Even if equity weighting—for both the costs and benefits of emissions reductions—is 

appropriate when considering the utility or welfare effects of international action, the interagency group concluded 

that it should not be used in developing an SCC for use in regulatory policy at this time. h Based on 2008 GDP (in current US dollars) from the World Bank Development Indicators Report.

14A-12

 
hydrofluorocarbons, perfluorocarbons, and sulfur hexafluoride. The climate impact of these 

gases is commonly discussed in terms of their 100-year global warming potential (GWP). GWP 

measures the ability of different gases to trap heat in the atmosphere (i.e., radiative forcing per 

unit of mass) over a particular timeframe relative to CO2. However, because these gases differ in 

both radiative forcing and atmospheric lifetimes, their relative damages are not constant over 

time. For example, because methane has a short lifetime, its impacts occur primarily in the near 

term and thus are not discounted as heavily as those caused by longer-lived gases. Impacts other 

than temperature change also vary across gases in ways that are not captured by GWP. For 

instance, CO2 emissions, unlike methane and other greenhouse gases, contribute to ocean 

acidification. Likewise, damages from methane emissions are not offset by the positive effect of 

CO2 fertilization. Thus, transforming gases into CO2-equivalents using GWP, and then 

multiplying the carbon-equivalents by the SCC, would not result in accurate estimates of the 

social costs of non-CO2 gases.

In light of these limitations, and the significant contributions of non-CO2 emissions to 

climate change, further research is required to link non-CO2 emissions to economic impacts.

Such work would feed into efforts to develop a monetized value of reductions in non-CO2

greenhouse gas emissions. As part of ongoing work to further improve the SCC estimates, the 

interagency group hopes to develop methods to value these other greenhouse gases. The goal is 

to develop these estimates by the time we issue revised SCC estimates for CO2 emissions.

14A.4.4 Equilibrium Climate Sensitivity

Equilibrium climate sensitivity (ECS) is a key input parameter for the DICE, PAGE, and 

FUND models.iIt is defined as the long-term increase in the annual global-average surface 

temperature from a doubling of atmospheric CO2 concentration relative to pre-industrial levels 

(or stabilization at a concentration of approximately 550 parts per million (ppm)). Uncertainties 

in this important parameter have received substantial attention in the peer-reviewed literature.

The most authoritative statement about equilibrium climate sensitivity appears in the 

Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC):

Basing our assessment on a combination of several independent lines of evidence...including 

observed climate change and the strength of known feedbacks simulated in [global climate 

models], we conclude that the global mean equilibrium warming for doubling CO2, or 

‘equilibrium climate sensitivity,’ is likely to lie in the range 2 °C to 4.5 °C, with a most likely 

value of about 3 °C. Equilibrium climate sensitivity is very likely larger than 1.5 °C.j

For fundamental physical reasons as well as data limitations, values substantially higher than 

4.5 °C still cannot be excluded, but agreement with observations and proxy data is generally 

i

The equilibrium climate sensitivity includes the response of the climate system to increased greenhouse gas 

concentrations over the short to medium term (up to 100–200 years), but it does not include long-term feedback 

effects due to possible large-scale changes in ice sheets or the biosphere, which occur on a time scale of many 

hundreds to thousands of years (e.g., Hansen et al. 2007).

j

This is in accord with the judgment that it “is likely to lie in the range 2 °C to 4.5 °C” and the IPCC definition of 

“likely” as greater than 66 percent probability (Le Treut et al. 2007). “Very likely” indicates a greater than 

90 percent probability.

14A-13

 
worse for those high values than for values in the 2 °C to 4.5 °C range. (Meehl et al. 2007, 

p. 799)

After consulting with several lead authors of this chapter of the IPCC report, the 

interagency workgroup selected four candidate probability distributions and calibrated them to 

be consistent with the above statement: Roe and Baker (2007), log-normal, gamma, and Weibull.

Table 14A.4.1 gives summary statistics for the four calibrated distributions.

Table 14A.4.1 Summary Statistics for Four Calibrated Climate Sensitivity Distributions

Rank Roe & Baker Log-Normal Gamma Weibull

Pr(ECS < 1.5 °C) 0.013 0.050 0.070 0.102

Pr(2 °C < ECS < 4.5 °C) 0.667 0.667 0.667 0.667

5th Percentile 1.72 1.49 1.37 1.13

10th Percentile 1.91 1.74 1.65 1.48

Mode 2.34 2.52 2.65 2.90

Median (50th percentile) 3.00 3.00 3.00 3.00

Mean 3.50 3.28 3.19 3.07

90th Percentile 5.86 5.14 4.93 4.69

95th Percentile 7.14 5.97 5.59 5.17

Each distribution was calibrated by applying three constraints from the IPCC:

1. a median equal to 3 °C, to reflect the judgment of “a most likely value of about 

3 °C;”k

2. two-thirds probability that the equilibrium climate sensitivity lies between 2 and 

4.5 °C; and

3. zero probability that it is less than 0 °C or greater than 10 °C (Hegerl et al. 2006, 

p. 721).

We selected the calibrated Roe and Baker distribution from the four candidates for two 

reasons. First, the Roe and Baker distribution is the only one of the four that is based on a 

theoretical understanding of the response of the climate system to increased greenhouse gas 

concentrations (Roe and Baker 2007; Roe 2008). In contrast, the other three distributions are 

mathematical functions that are arbitrarily chosen based on simplicity, convenience, and general 

shape. The Roe and Baker distribution results from three assumptions about climate response: 

(1) absent feedback effects, the equilibrium climate sensitivity is equal to 1.2 °C; (2) feedback 

factors are proportional to the change in surface temperature; and (3) uncertainties in feedback 

factors are normally distributed. There is widespread agreement on the first point and the second 

and third points are common assumptions. 

k Strictly speaking, “most likely” refers to the mode of a distribution rather than the median, but common usage 

would allow the mode, median, or mean to serve as candidates for the central or “most likely” value and the IPCC 

report is not specific on this point. For the distributions we considered, the median was between the mode and the 

mean. For the Roe and Baker distribution, setting the median equal to 3 °C, rather than the mode or mean, gave a 

95th percentile that is more consistent with IPCC judgments and the literature. For example, setting the mean and 

mode equal to 3 °C produced 95th percentiles of 5.6 and 8.6 °C, respectively, which are in the lower and upper end 

of the range in the literature. Finally, the median is closer to 3 °C than is the mode for the truncated distributions 

selected by the IPCC (Hegerl et al. 2006); the average median is 3.1 °C and the average mode is 2.3 °C, which is 

most consistent with a Roe and Baker distribution with the median set equal to 3 °C.

14A-14

 
Second, the calibrated Roe and Baker distribution better reflects the IPCC judgment that 

“values substantially higher than 4.5°C still cannot be excluded.” Although the IPCC made no 

quantitative judgment, the 95th percentile of the calibrated Roe & Baker distribution (7.1 °C) is 

much closer to the mean and the median (7.2 °C) of the 95th percentiles of 21 previous studies 

summarized by Newbold and Daigneault (2009). It is also closer to the mean (7.5 °C) and 

median (7.9 °C) of the nine truncated distributions examined by the IPCC (Hegerl et al. 2006) 

than are the 95th percentiles of the three other calibrated distributions (5.2–6.0 °C).

Finally, we note the IPCC judgment that the equilibrium climate sensitivity “is very 

likely larger than 1.5°C.” Although the calibrated Roe & Baker distribution, for which the 

probability of equilibrium climate sensitivity being greater than 1.5 °C is almost 99 percent, is 

not inconsistent with the IPCC definition of “very likely” as “greater than 90 percent 

probability,” it reflects a greater degree of certainty about very low values of ECS than was 

expressed by the IPCC. 

To show how the calibrated Roe and Baker distribution compares to different estimates 

of the probability distribution function of equilibrium climate sensitivity in the empirical 

literature, Figure 14A.4.3 overlays it on Figure 14A.9.2 from the IPCC Fourth Assessment 

Report. These functions are scaled to integrate to unity between 0 °C and 10 °C. The horizontal 

bars show the respective 5 percent to 95 percent ranges; dots indicate the median estimate.l

Figure 14A.4.3 Estimates of the Probability Density Function for Equilibrium Climate 

Sensitivity

l

The estimates based on instrumental data are from Andronova and Schlesinger (2001), Forest et al. (2002; dashed 

line, anthropogenic forcings only), Forest et al. (2006; solid line, anthropogenic and natural forcings), Gregory et al. 

(2002), Knutti et al. (2002), Frame et al. (2005), and Forster and Gregory (2006). Hegerl et al. (2006) are based on 

multiple palaeoclimatic reconstructions of north hemisphere mean temperatures over the last 700 years. Also shown 

are the 5–95 percent approximate ranges for two estimates from the last glacial maximum (dashed, Annan et al. 

2005; solid, Schneider von Deimling et al. 2006), which are based on models with different structural properties.

14A-15

 
14A.4.5 Socio-Economic and Emissions Trajectories

Another key issue considered by the interagency group is how to select the set of socioeconomic and emissions parameters for use in PAGE, DICE, and FUND. Socio-economic 

pathways are closely tied to climate damages because, all else equal, more and wealthier people 

tend to emit more greenhouse gases and also have a higher (absolute) willingness to pay to avoid 

climate disruptions. For this reason, we consider how to model several input parameters in 

tandem: GDP, population, CO2 emissions, and non-CO2 radiative forcing. A wide variety of 

scenarios have been developed and used for climate change policy simulations (e.g., SRES 2000, 

CCSP 2007, EMF 2009). In determining which scenarios are appropriate for inclusion, we aimed 

to select scenarios that span most of the plausible ranges of outcomes for these variables. 

To accomplish this task in a transparent way, we decided to rely on the recent Stanford 

Energy Modeling Forum exercise, EMF-22, which uses ten well-recognized models to evaluate 

substantial, coordinated global action to meet specific stabilization targets. A key advantage of 

relying on these data is that GDP, population, and emission trajectories are internally consistent 

for each model and scenario evaluated. The EMF-22 modeling effort also is preferable to the 

IPCC SRES due to their age (SRES were developed in 1997) and the fact that 3 of 4 of the SRES 

scenarios are now extreme outliers in one or more variables. Although the EMF-22 scenarios 

have not undergone the same level of scrutiny as the SRES scenarios, they are recent, peerreviewed, published, and publicly available.

To estimate the SCC for use in evaluating domestic policies that will have a small effect 

on global cumulative emissions, we use socio-economic and emission trajectories that span a 

range of plausible scenarios. Five trajectories were selected from EMF-22 (Table 14A.4.2 ). Four 

of these represent potential business-as-usual (BAU) growth in population, wealth, and 

emissions and are associated with CO2 (only) concentrations ranging from 612 to 889 ppm in 

2100. One represents an emissions pathway that achieves stabilization at 550 ppm CO2e (i.e., 

CO2-only concentrations of 425–484 ppm or a radiative forcing of 3.7 W/m2) in 2100, a lowerthan-BAU trajectory.m Out of the 10 models included in the EMF-22 exercise, we selected the 

trajectories used by MiniCAM, MESSAGE, IMAGE, and the optimistic scenario from MERGE. 

For the BAU pathways, we used the GDP, population, and emission trajectories from each of 

these four models. For the 550 ppm CO2e scenario, we averaged the GDP, population, and 

emission trajectories implied by these same four models.

m Such an emissions path would be consistent with widespread action by countries to mitigate GHG emissions, 

though it could also result from technological advances. It was chosen because it represents the most stringent case 

analyzed by the EMF-22 where all the models converge: a 550 ppm, not to exceed, full participation scenario.

14A-16

 
Table 14A.4.2 Socioeconomic and Emissions Projections from Select EMF-22 Reference 

Scenarios

Reference Fossil and Industrial CO2 Emissions

GtCO2/yr

EMF – 22 Based Scenarios 2000 2010 2020 2030 2050 2100

IMAGE 26.6 31.9 36.9 40.0 45.3 60.1

MERGE Optimistic 24.6 31.5 37.6 45.1 66.5 117.9

MESSAGE 26.8 29.2 37.6 42.1 43.5 42.7

MiniCAM 26.5 31.8 38.0 45.1 57.8 80.5

550 ppm average 26.2 31.1 33.2 32.4 20.0 12.8

Reference GDP

market exchange rates in trillion 2005$n

EMF – 22 Based Scenarios 2000 2010 2020 2030 2050 2100

IMAGE 38.6 53.0 73.5 97.2 156.3 396.6

MERGE Optimistic 36.3 45.9 59.7 76.8 122.7 268.0

MESSAGE 38.1 52.3 69.4 91.4 153.7 334.9

MiniCAM 36.1 47.4 60.8 78.9 125.7 369.5

550 ppm average 37.1 49.6 65.6 85.5 137.4 337.9

Global Population

billions

EMF – 22 Based Scenarios 2000 2010 2020 2030 2050 2100

IMAGE 6.1 6.9 7.6 8.2 9.0 9.1

MERGE Optimistic 6.0 6.8 7.5 8.2 9.0 9.7

MESSAGE 6.1 6.9 7.7 8.4 9.4 10.4

MiniCAM 6.0 6.8 7.5 8.1 8.8 8.7

550 ppm average 6.1 6.8 7.6 8.2 8.7 9.1

We explore how sensitive the SCC is to various assumptions about how the future will 

evolve without prejudging what is likely to occur. The interagency group considered formally 

assigning probability weights to different states of the world, but this proved challenging to do in 

an analytically rigorous way given the dearth of information on the likelihood of a full range of 

future socio-economic pathways.

There are a number of caveats. First, EMF BAU scenarios represent the modelers’ 

judgment of the most likely pathway absent mitigation policies to reduce greenhouse gas 

emissions, rather than the wider range of possible outcomes. Nevertheless, these views of the 

n While the EMF-22 models used market exchange rates (MER) to calculate global GDP, it is also possible to use 

purchasing power parity (PPP), which takes into account the different price levels across countries, so it more 

accurately describes relative standards of living across countries. MERs tend to make low-income countries appear 

poorer than they actually are. Because many models assume convergence in per capita income over time, use of 

MER-adjusted GDP gives rise to projections of higher economic growth in low income countries. There is an 

ongoing debate about how much this will affect estimated climate impacts. Critics of the use of MER argue that it 

leads to overstated economic growth and hence a significant upward bias in projections of greenhouse gas 

emissions, and unrealistically high future temperatures (e.g., Castles and Henderson 2003). Others argue that 

convergence of the emissions-intensity gap across countries at least partially offset the overstated income gap so that 

differences in exchange rates have less of an effect on emissions (Holtsmark and Alfsen, 2005; Tol, 2006). 

Nordhaus (2007b) argues that the ideal approach is to use superlative PPP accounts (i.e., using cross-sectional PPP 

measures for relative incomes and outputs and national accounts price and quantity indexes for time-series 

extrapolations). However, he notes that it important to keep this debate in perspective; it is by no means clear that 

exchange-rate-conversion issues are as important as uncertainties about population, technological change, or the 

many geophysical uncertainties.

14A-17

 
most likely outcome span a wide range, from the more optimistic (e.g., abundant low-cost, lowcarbon energy) to more pessimistic (e.g., constraints on the availability of nuclear and 

renewables).o Second, the socio-economic trajectories associated with a 550 ppm CO2e 

concentration scenario are not derived from an assessment of what policy is optimal from a 

benefit-cost standpoint. Rather, it is indicative of one possible future outcome. The emission 

trajectories underlying some BAU scenarios (e.g., MESSAGE’s 612 ppm) also are consistent 

with some modest policy action to address climate change.p We chose not to include socioeconomic trajectories that achieve even lower GHG concentrations at this time, given the 

difficulty many models had in converging to meet these targets.

For comparison purposes, the Energy Information Agency in its 2009 Annual Energy 

Outlook projected that global CO2 emissions will grow to 30.8, 35.6, and 40.4 gigatons in 2010, 

2020, and 2030, respectively, while world GDP is projected to be $51.8, $71.0 and $93.9 trillion 

(2005$ using market exchange rates) in 2010, 2020, and 2030, respectively. These projections 

are consistent with one or more EMF-22 scenarios. Likewise, the United Nations’ 2008 

Population Prospect projects population will grow from 6.1 billion people in 2000 to 9.1 billion 

people in 2050, which is close to the population trajectories for the IMAGE, MiniCAM, and 

MERGE models.

In addition to fossil and industrial CO2 emissions, each EMF scenario provides 

projections of methane, nitrous oxide, fluorinated greenhouse gases, and net land use CO2

emissions out to 2100. These assumptions also are used in the three models while retaining the 

default radiative forcings due to other factors (e.g., aerosols and other gases). See the Appendix 

for greater detail.

14A.4.6 Discount Rate

The choice of a discount rate, especially over long periods of time, raises highly 

contested and exceedingly difficult questions of science, economics, philosophy, and law.

Although it is well understood that the discount rate has a large influence on the current value of 

future damages, there is no consensus about what rates to use in this context. Because CO2

emissions are long-lived, subsequent damages occur over many years. In calculating the SCC, 

we first estimate the future damages to agriculture, human health, and other market and nonmarket sectors from an additional unit of CO2 emitted in a particular year in terms of reduced 

consumption (or consumption equivalents) due to the impacts of elevated temperatures, as 

represented in each of the three IAMs. Then we discount the stream of future damages to its 

present value in the year when the additional unit of emissions was released using the selected 

discount rate, which is intended to reflect society’s marginal rate of substitution between 

consumption in different time periods.

o For instance, in the MESSAGE model’s reference case total primary energy production from nuclear, biomass, and 

non-biomass renewables is projected to increase from about 15 percent of total primary energy in 2000 to 54 percent 

in 2100. In comparison, the MiniCAM reference case shows 10 percent in 2000 and 21 percent in 2100. 

p For example, MiniCAM projects if all non-US OECD countries reduce CO2 emissions to 83 percent below 2005 

levels by 2050 (per the G-8 agreement) but all other countries continue along a BAU path CO2 concentrations in 

2100 would drop from 794 ppmv in its reference case to 762 ppmv.

14A-18

 
For rules with both intra- and intergenerational effects, agencies traditionally employ 

constant discount rates of both 3 percent and 7 percent in accordance with OMB Circular A-4. 

As Circular A-4 acknowledges, however, the choice of discount rate for intergenerational 

problems raises distinctive problems and presents considerable challenges. After reviewing those 

challenges, Circular A-4 states, “If your rule will have important intergenerational benefits or 

costs you might consider a further sensitivity analysis using a lower but positive discount rate in 

addition to calculating net benefits using discount rates of 3 and 7 percent.” For the specific 

purpose of developing the SCC, we adapt and revise that approach here.

Arrow et al. (1996) outlined two main approaches to determine the discount rate for 

climate change analysis, which they labeled “descriptive” and “prescriptive.” The descriptive 

approach reflects a positive (non-normative) perspective based on observations of people’s 

actual choices—e.g., savings versus consumption decisions over time, and allocations of savings 

among more and less risky investments. Advocates of this approach generally call for inferring 

the discount rate from market rates of return “because of a lack of justification for choosing a 

social welfare function that is any different than what decision makers [individuals] actually use” 

(Arrow et al. 1996).

One theoretical foundation for the cost-benefit analyses in which the social cost of carbon 

will be used—the Kaldor-Hicks potential-compensation test—also suggests that market rates 

should be used to discount future benefits and costs, because it is the market interest rate that 

would govern the returns potentially set aside today to compensate future individuals for climate 

damages that they bear (e.g., Just et al. 2004). As some have noted, the word “potentially” is an 

important qualification; there is no assurance that such returns will actually be set aside to 

provide compensation, and the very idea of compensation is difficult to define in the 

intergenerational context. On the other hand, societies provide compensation to future 

generations through investments in human capital and the resulting increase in knowledge, as 

well as infrastructure and other physical capital.

The prescriptive approach specifies a social welfare function that formalizes the 

normative judgments that the decision-maker wants explicitly to incorporate into the policy 

evaluation—e.g., how inter-personal comparisons of utility should be made, and how the welfare 

of future generations should be weighed against that of the present generation. Ramsey (1928), 

for example, has argued that it is “ethically indefensible” to apply a positive pure rate of time 

preference to discount values across generations, and many agree with this view.

Other concerns also motivate making adjustments to descriptive discount rates. In

particular, it has been noted that the preferences of future generations with regard to 

consumption versus environmental amenities may not be the same as those today, making the 

current market rate on consumption an inappropriate metric by which to discount future climaterelated damages. Others argue that the discount rate should be below market rates to correct for 

market distortions and uncertainties or inefficiencies in intergenerational transfers of wealth, 

which in the Kaldor-Hicks logic are presumed to compensate future generations for damage (a 

potentially controversial assumption, as noted above; Arrow et al. 1996, Weitzman 1999).

Further, a legitimate concern about both descriptive and prescriptive approaches is that 

they tend to obscure important heterogeneity in the population. The utility function that underlies 

14A-19
the prescriptive approach assumes a representative agent with perfect foresight and no credit 

constraints. This is an artificial rendering of the real world that misses many of the frictions that 

characterize individuals’ lives and indeed the available descriptive evidence supports this. For 

instance, many individuals smooth consumption by borrowing with credit cards that have 

relatively high rates. Some are unable to access traditional credit markets and rely on payday 

lending operations or other high cost forms of smoothing consumption. Whether one puts greater 

weight on the prescriptive or descriptive approach, the high interest rates that credit-constrained 

individuals accept suggest that some account should be given to the discount rates revealed by 

their behavior. 

We draw on both approaches but rely primarily on the descriptive approach to inform the 

choice of discount rate. With recognition of its limitations, we find this approach to be the most 

defensible and transparent given its consistency with the standard contemporary theoretical 

foundations of benefit-cost analysis and with the approach required by OMB’s existing guidance. 

The logic of this framework also suggests that market rates should be used for discounting future 

consumption-equivalent damages. Regardless of the theoretical approach used to derive the 

appropriate discount rate(s), we note the inherent conceptual and practical difficulties of 

adequately capturing consumption trade-offs over many decades or even centuries. While relying 

primarily on the descriptive approach in selecting specific discount rates, the interagency group 

has been keenly aware of the deeply normative dimensions of both the debate over discounting 

in the intergenerational context and the consequences of selecting one discount rate over another.

14A.4.6.1 Historically Observed Interest Rates

In a market with no distortions, the return to savings would equal the private return on 

investment, and the market rate of interest would be the appropriate choice for the social 

discount rate. In the real world risk, taxes, and other market imperfections drive a wedge 

between the risk-free rate of return on capital and the consumption rate of interest. Thus, the 

literature recognizes two conceptual discount concepts—the consumption rate of interest and the 

opportunity cost of capital.

According to OMB’s Circular A-4, it is appropriate to use the rate of return on capital 

when a regulation is expected to displace or alter the use of capital in the private sector. In this 

case, OMB recommends Agencies use a discount rate of 7 percent. When regulation is expected 

to primarily affect private consumption—for instance, via higher prices for goods and services—

a lower discount rate of 3 percent is appropriate to reflect how private individuals trade-off 

current and future consumption. 

The interagency group examined the economics literature and concluded that the 

consumption rate of interest is the correct concept to use in evaluating the benefits and costs of a 

marginal change in carbon emissions (Lind 1990, Arrow et al. 1996, Arrow 2000). The 

consumption rate of interest also is appropriate when the impacts of a regulation are measured in 

consumption (-equivalent) units, as is done in the three integrated assessment models used for 

estimating the SCC.

Individuals use a variety of savings instruments that vary with risk level, time horizon, 

and tax characteristics. The standard analytic framework used to develop intuition about the 

14A-20
discount rate typically assumes a representative agent with perfect foresight and no credit 

constraints. The risk-free rate is appropriate for discounting certain future benefits or costs, but 

the benefits calculated by IAMs are uncertain. To use the risk-free rate to discount uncertain 

benefits, these benefits first must be transformed into “certainty equivalents,” i.e., the maximum 

certain amount that we would exchange for the uncertain amount. However, the calculation of 

the certainty-equivalent requires first estimating the correlation between the benefits of the 

policy and baseline consumption.

If the IAM projections of future impacts represent expected values (not certaintyequivalent values), then the appropriate discount rate generally does not equal the risk-free rate. 

If the benefits of the policy tend to be high in those states of the world in which consumption is 

low, then the certainty-equivalent benefits will be higher than the expected benefits (and vice 

versa). Since many (though not necessarily all) of the important impacts of climate change will 

flow through market sectors such as agriculture and energy, and since willingness to pay for 

environmental protections typically increases with income, we might expect a positive (though 

not necessarily perfect) correlation between the net benefits from climate policies and market 

returns. This line of reasoning suggests that the proper discount rate would exceed the riskless 

rate. Alternatively, a negative correlation between the returns to climate policies and market 

returns would imply that a discount rate below the riskless rate is appropriate.

This discussion suggests that both the post-tax riskless and risky rates can be used to 

capture individuals’ consumption-equivalent interest rate. As a measure of the post-tax riskless 

rate, we calculate the average real return from Treasury notes over the longest time period 

available (those from Newell and Pizer 2003) and adjust for Federal taxes (the average marginal 

rate from tax years 2003 through 2006 is around 27 percent).q This calculation produces a real 

interest rate of about 2.7 percent, which is roughly consistent with Circular A-4’s 

recommendation to use 3 percent to represent the consumption rate of interest.r A measure of the 

post-tax risky rate for investments whose returns are positively correlated with overall equity 

market returns can be obtained by adjusting pre-tax rates of household returns to risky 

investments (approximately 7 percent) for taxes yields a real rate of roughly 5 percent.s

14A.4.6.2 The Ramsey Equation

Ramsey discounting also provides a useful framework to inform the choice of a discount 

rate. Under this approach, the analyst applies either positive or normative judgments in selecting 

values for the key parameters of the Ramsey equation: η (coefficient of relative risk aversion or 

q The literature argues for a risk-free rate on government bonds as an appropriate measure of the consumption rate of 

interest. Arrow (2000) suggests that it is roughly 3-4 percent. OMB cites evidence of a 3.1 percent pre-tax rate for 

10-year Treasury notes in the A-4 guidance. Newell and Pizer (2003) find real interest rates between 3.5 and 

4 percent for 30-year Treasury securities. r The positive approach reflects how individuals make allocation choices across time, but it is important to keep in 

mind that we wish to reflect preferences for society as a whole, which generally has a longer planning horizon. s Cambell et al. (2001) estimates that the annual real return from stocks for 1900-1995 was about 7 percent. The 

annual real rate of return for the S&P 500 from 1950–2008 was about 6.8 percent. In the absence of a better way to 

population-weight the tax rates, we use the middle of the 20–40 percent range to derive a post-tax interest rate 

(Kotlikoff and Rapson 2006).

14A-21

 
elasticity of the marginal utility of consumption) and ρ (pure rate of time preference).t

These are 

then combined with g (growth rate of per-capita consumption) to equal the interest rate at which 

future monetized damages are discounted: ρ + η∙g.

u In the simplest version of the Ramsey model, 

with an optimizing representative agent with perfect foresight, what we are calling the “Ramsey 

discount rate,” ρ + η∙g, will be equal to the rate of return to capital, i.e., the market interest rate.

A review of the literature provides some guidance on reasonable parameter values for the 

Ramsey discounting equation, based on both prescriptive and descriptive approaches. 

• η. Most papers in the climate change literature adopt values for η in the range of 0.5 to 3 

(Weitzman cites plausible values as those ranging from 1 to 4), although not all authors 

articulate whether their choice is based on prescriptive or descriptive reasoning.v

Dasgupta (2008) argues that η should be greater than 1 and may be as high as 3, since η

equal to 1 suggests savings rates that do not conform to observed behavior. 

• ρ. With respect to the pure rate of time preference, most papers in the climate change 

literature adopt values for ρ in the range of 0 to 3 percent per year. The very low rates 

tend to follow from moral judgments involving intergenerational neutrality. Some have 

argued that to use any value other than ρ = 0 would unjustly discriminate against future 

generations (e.g., Arrow et al. 1996, Stern 2006). However, even in an inter-generational 

setting, it may make sense to use a small positive pure rate of time preference because of 

the small probability of unforeseen cataclysmic events (Stern 2006).

• g. A commonly accepted approximation is around 2 percent per year. For the socioeconomic scenarios used for this exercise, the EMF models assume that g is about 

1.5–2 percent to 2100.

t

The parameter ρ measures the pure rate of time preference: people’s behavior reveals a preference for an increase 

in utility today versus the future. Consequently, it is standard to place a lower weight on utility in the future. The 

parameter η captures diminishing marginal utility: consumption in the future is likely to be higher than consumption 

today, so diminishing marginal utility of consumption implies that the same monetary damage will cause a smaller 

reduction of utility for wealthier individuals, either in the future or in current generations. If η = 0, then a one dollar 

increase in income is equally valuable regardless of level of income; if η = 1, then a one percent increase in income 

is equally valuable no matter the level of income; and if η > 1, then a one percent increase in income is less valuable 

to wealthier individuals. u In this case, g could be taken from the selected EMF socioeconomic scenarios or alternative assumptions about the 

rate of consumption growth.

v Empirical estimates of η span a wide range of values. A benchmark value of 2 is near the middle of the range of 

values estimated or used by Szpiro (1986), Hall and Jones (2007), Arrow (2007), Dasgupta (2006, 2008), Weitzman 

(2007, 2009), and Nordhaus (2008). However, Chetty (2006) developed a method of estimating η using data on 

labor supply behavior. He shows that existing evidence of the effects of wage changes on labor supply imposes a 

tight upper bound on the curvature of utility over wealth (CRRA < 2) with the mean implied value of 0.71 and 

concludes that the standard expected utility model cannot generate high levels of risk aversion without contradicting 

established facts about labor supply. Recent work has jointly estimated the components of the Ramsey equation. 

Evans and Sezer (2005) estimate η = 1.49 for 22 OECD countries. They also estimate ρ = 1.08 percent per year 

using data on mortality rates. Anthoff et al. (2009b) estimate η = 1.18, and ρ = 1.4 percent. When they multiply the 

bivariate probability distributions from their work and Evans and Sezer (2005) together, they find η = 1.47, and 

ρ = 1.07. 

14A-22

 
Some economists and non-economists have argued for constant discount rates below 

2 percent based on the prescriptive approach. When grounded in the Ramsey framework, 

proponents of this approach have argued that a ρ of zero avoids giving preferential treatment to 

one generation over another. The choice of η has also been posed as an ethical choice linked to 

the value of an additional dollar in poorer countries compared to wealthier ones. Stern (2006) 

applies this perspective through his choice of ρ = 0.1 percent per year, η = 1 and g = 1.3 percent 

per year, which yields an annual discount rate of 1.4 percent. In the context of permanent income 

savings behavior, however, Stern’s assumptions suggest that individuals would save 93 percent 

of their income.w

Recently, Stern (2008) revisited the values used in Stern (2006), stating that there is a 

case to be made for raising η due to the amount of weight lower values place on damages far in 

the future (over 90 percent of expected damages occur after 2200 with η = 1). Using Stern’s 

assumption that ρ = 0.1 percent, combined with a η of 1.5 to 2 and his original growth rate, 

yields a discount rate greater 2 percent.

We conclude that arguments made under the prescriptive approach can be used to justify 

discount rates between roughly 1.4 and 3.1 percent. In light of concerns about the most 

appropriate value for η, we find it difficult to justify rates at the lower end of this range under the 

Ramsey framework.

14A.4.6.3 Accounting for Uncertainty in the Discount Rate

While the consumption rate of interest is an important driver of the benefits estimate, it is 

uncertain over time. Ideally, we would formally model this uncertainty, just as we do for climate 

sensitivity. Weitzman (1998, 2001) showed theoretically and Newell and Pizer (2003) and 

Panipoulou et al. (2004) confirm empirically that discount rate uncertainty can have a large 

effect on net present values. A main result from these studies is that if there is a persistent 

element to the uncertainty in the discount rate (e.g., the rate follows a random walk), then it will 

result in an effective (or certainty-equivalent) discount rate that declines over time. 

Consequently, lower discount rates tend to dominate over the very long term (Weitzman 1998, 

1999, 2001; Newell and Pizer 2003; Panipoulou et al. (2004); Gollier 2008; Summers and 

Zeckhauser 2008; and Gollier and Weitzman 2009). 

The proper way to model discount rate uncertainty remains an active area of research.

Newell and Pizer (2003) employ a model of how long-term interest rates change over time to 

forecast future discount rates. Their model incorporates some of the basic features of how 

interest rates move over time, and its parameters are estimated based on historical observations 

of long-term rates. Subsequent work on this topic, most notably Panipoulou et al. (2004), uses 

more general models of interest rate dynamics to allow for better forecasts. Specifically, the 

volatility of interest rates depends on whether rates are currently low or high and variation in the 

level of persistence over time.

w Stern (2008) argues that building in a positive rate of exogenous technical change over time reduces the implied 

savings rate and that η at or above 2 are inconsistent with observed behavior with regard to equity. (At the same 

time, adding exogenous technical change—all else equal—would increase g as well.)

14A-23

 
While Newell and Pizer (2003) and Panipoulou et al. (2004) attempt formally to model 

uncertainty in the discount rate, others argue for a declining scale of discount rates applied over 

time (e.g., Weitzman 2001, and the UK’s “Green Book” for regulatory analysis). This approach 

uses a higher discount rate initially, but applies a graduated scale of lower discount rates further 

out in time.x A key question that has emerged with regard to both of these approaches is the 

trade-off between potential time inconsistency and giving greater weight to far future outcomes 

(see the EPA Science Advisory Board’s recent comments on this topic as part of its review of 

their Guidelines for Economic Analysis).y

14A.4.6.4 The Discount Rates Selected for Estimating SCC

In light of disagreement in the literature on the appropriate market interest rate to use in 

this context and uncertainty about how interest rates may change over time, we use three 

discount rates to span a plausible range of certainty-equivalent constant discount rates: 2.5, 3, 

and 5 percent per year. Based on the review in the previous sections, the interagency workgroup 

determined that these three rates reflect reasonable judgments under both descriptive and 

prescriptive approaches.

The central value (3 percent) is consistent with estimates provided in the economics 

literature and OMB’s Circular A-4 guidance for the consumption rate of interest. As previously 

mentioned, the consumption rate of interest is the correct discounting concept to use when future 

damages from elevated temperatures are estimated in consumption-equivalent units. Further, 

3 percent roughly corresponds to the after-tax riskless interest rate. The upper value of 5 percent 

is included to represent the possibility that climate damages are positively correlated with market 

returns. Additionally, this discount rate may be justified by the high interest rates that many 

consumers use to smooth consumption across periods.

The low value (2.5 percent) is included to incorporate the concern that interest rates are 

highly uncertain over time. It represents the average certainty-equivalent rate using the meanreverting and random walk approaches from Newell and Pizer (2003) starting at a discount rate 

of 3 percent. Using this approach, the certainty equivalent is about 2.2 percent using the random 

walk model and 2.8 percent using the mean reverting approach.z Without giving preference to a 

particular model, the average of the two rates is 2.5 percent. Further, a rate below the riskless 

rate would be justified if climate investments are negatively correlated with the overall market 

rate of return. Use of this lower value also responds to certain judgments using the prescriptive 

x For instance, the UK applies a discount rate of 3.5 percent to the first 30 years; 3 percent for years 31–75; 

2.5 percent for years 76–125; 2 percent for years 126–200; 1.5 percent for years 201–300; and 1 percent after 

300 years. As a sensitivity, it recommends a discount rate of 3 percent for the first 30 years, also decreasing over 

time. 

y Uncertainty in future damages is distinct from uncertainty in the discount rate. Weitzman (2008) argues that 

Stern’s choice of a low discount rate was “right for the wrong reasons.” He demonstrates how the damages from a 

low probability, catastrophic event far in the future dominate the effect of the discount rate in a present value 

calculation and result in an infinite willingness-to-pay for mitigation today. Newbold and Daigneault, (2009) and 

Nordhaus (2009) find that Weitzman’s result is sensitive to the functional forms chosen for climate sensitivity, 

utility, and consumption. Summers and Zeckhauser (2008) argue that uncertainty in future damages can also work in 

the other direction by increasing the benefits of waiting to learn the appropriate level of mitigation required. 

z Calculations done by Pizer et al. using the original simulation program from Newell and Pizer (2003).

14A-24

 
or normative approach and to ethical objections that have been raised about rates of 3 percent or 

higher.

14A.5 REVISED SCC ESTIMATES

Our general approach to estimating SCC values is to run the three integrated assessment 

models (FUND, DICE, and PAGE) using the following inputs agreed upon by the interagency 

group:

• A Roe and Baker distribution for the climate sensitivity parameter bounded between 0 

and 10 with a median of 3 °C and a cumulative probability between 2 and 4.5 °C of twothirds.

• Five sets of GDP, population and carbon emissions trajectories based on EMF-22.

• Constant annual discount rates of 2.5, 3, and 5 percent.

Because the climate sensitivity parameter is modeled probabilistically, and because 

PAGE and FUND incorporate uncertainty in other model parameters, the final output from each 

model run is a distribution over the SCC in year t. 

For each of the IAMS, the basic computational steps for calculating the SCC in a 

particular year t are:

1. Input the path of emissions, GDP, and population from the selected EMF-22 scenarios, 

and the extrapolations based on these scenarios for post-2100 years.

2. Calculate the temperature effects and (consumption-equivalent) damages in each year 

resulting from the baseline path of emissions.

a. In PAGE, the consumption-equivalent damages in each period are calculated as a 

fraction of the EMF GDP forecast, depending on the temperature in that period 

relative to the pre-industrial average temperature in each region.

b. In FUND, damages in each period depend on both the level and the rate of 

temperature change in that period.

c. In DICE, temperature affects both consumption and investment, so we first adjust the 

EMF GDP paths as follows: Using the Cobb-Douglas production function with the 

DICE2007 parameters, we extract the path of exogenous technical change implied by 

the EMF GDP and population paths, then we recalculate the baseline GDP path 

taking into account climate damages resulting from the baseline emissions path.

3. Add an additional unit of carbon emissions in year t. (The exact unit varies by model.)

4. Recalculate the temperature effects and damages expected in all years beyond t resulting 

from this adjusted path of emissions, as in step 2. 

5. Subtract the damages computed in step 2 from those in step 4 in each year. (DICE is run 

in 10 year time steps, FUND in annual time steps, while the time steps in PAGE vary.)

14A-25
6. Discount the resulting path of marginal damages back to the year of emissions using the 

agreed upon fixed discount rates.

7. Calculate the SCC as the net present value of the discounted path of damages computed 

in step 6, divided by the unit of carbon emissions used to shock the models in step 3.

8. Multiply by 12/44 to convert from dollars per ton of carbon to dollars per ton of CO2

(2007 dollars) in DICE and FUND. (All calculations are done in tons of CO2 in PAGE).

The steps above were repeated in each model for multiple future years to cover the time 

horizons anticipated for upcoming rulemaking analysis. To maintain consistency across the three 

IAMs, climate damages are calculated as lost consumption in each future year.

It is important to note that each of the three models has a different default end year. The 

default time horizon is 2200 for PAGE, 2595 for DICE, and 3000 for the latest version of FUND.

This is an issue for the multi-model approach because differences in SCC estimates may arise 

simply due to the model time horizon. Many consider 2200 too short a time horizon because it 

could miss a significant fraction of damages under certain assumptions about the growth of 

marginal damages and discounting, so each model is run here through 2300. This step required a 

small adjustment in the PAGE model only. This step also required assumptions about GDP, 

population, and greenhouse gas emission trajectories after 2100, the last year for which these 

data are available from the EMF-22 models. (A more detailed discussion of these assumptions is 

included in the Appendix.)

This exercise produces 45 separate distributions of the SCC for a given year, the product 

of 3 models, 3 discount rates, and 5 socioeconomic scenarios. This is clearly too many separate 

distributions for consideration in a regulatory impact analysis. 

To produce a range of plausible estimates that still reflects the uncertainty in the 

estimation exercise, the distributions from each of the models and scenarios are equally weighed 

and combined to produce three separate probability distributions for SCC in a given year, one for 

each assumed discount rate. These distributions are then used to define a range of point estimates 

for the global SCC. In this way, no integrated assessment model or socioeconomic scenario is 

given greater weight than another. Because the literature shows that the SCC is quite sensitive to 

assumptions about the discount rate, and because no consensus exists on the appropriate rate to 

use in an intergenerational context, we present SCCs based on the average values across models 

and socioeconomic scenarios for each discount rate. 

The interagency group selected four SCC values for use in regulatory analyses. Three 

values are based on the average SCC across models and socio-economic and emissions scenarios 

at the 2.5-, 3-, and 5-percent discount rates. The fourth value is included to represent the higherthan-expected economic impacts from climate change further out in the tails of the SCC 

distribution. For this purpose, we use the SCC value for the 95th percentile at a 3-percent 

discount rate. (The full set of distributions by model and scenario combination is included in the 

Appendix.) As noted above, the 3-percent discount rate is the central value, and so the central 

value that emerges is the average SCC across models at the 3-percent discount rate. For purposes 

14A-26
of capturing the uncertainties involved in regulatory impact analysis, we emphasize the 

importance and value of considering the full range.

As previously discussed, low probability, high impact events are incorporated into the 

SCC values through explicit consideration of their effects in two of the three models as well as 

the use of a probability density function for equilibrium climate sensitivity. Treating climate 

sensitivity probabilistically results in more high temperature outcomes, which in turn lead to 

higher projections of damages. Although FUND does not include catastrophic damages (in 

contrast to the other two models), its probabilistic treatment of the equilibrium climate sensitivity 

parameter will directly affect the non-catastrophic damages that are a function of the rate of 

temperature change.

In Table 14A.5.1, we begin by presenting SCC estimates for 2010 by model, scenario, 

and discount rate to illustrate the variability in the SCC across each of these input parameters. As 

expected, higher discount rates consistently result in lower SCC values, while lower discount 

rates result in higher SCC values for each socioeconomic trajectory. It is also evident that there 

are differences in the SCC estimated across the three main models. For these estimates, FUND 

produces the lowest estimates, while PAGE generally produces the highest estimates. 

Table 14A.5.1 Disaggregated Social Cost of CO2 Values by Model, Socio-Economic 

Trajectory, and Discount Rate for 2010 (2007$)

Discount rate: 5% 3% 2.5% 3%

Model Scenario Avg Avg Avg 95th

DICE IMAGE 10.8 35.8 54.2 70.8

MERGE 7.5 22.0 31.6 42.1

Message 9.8 29.8 43.5 58.6

MiniCAM 8.6 28.8 44.4 57.9

550 Average 8.2 24.9 37.4 50.8

PAGE IMAGE 8.3 39.5 65.5 142.4

MERGE 5.2 22.3 34.6 82.4

Message 7.2 30.3 49.2 115.6

MiniCAM 6.4 31.8 54.7 115.4

550 Average 5.5 25.4 42.9 104.7

FUND IMAGE -1.3 8.2 19.3 39.7

MERGE -0.3 8.0 14.8 41.3

Message -1.9 3.6 8.8 32.1

MiniCAM -0.6 10.2 22.2 42.6

550 Average -2.7 -0.2 3.0 19.4

These results are not surprising when compared to the estimates in the literature for the 

latest versions of each model. For example, adjusting the values from the literature that were 

used to develop interim SCC values to 2007 dollars for the year 2010 (assuming, as we did for 

the interim process, that SCC grows at 3 percent per year), FUND yields SCC estimates at or 

near zero for a 5-percent discount rate and around $9 per ton for a 3-percent discount rate. There 

are far fewer estimates using the latest versions of DICE and PAGE in the literature: Using 

similar adjustments to generate 2010 estimates, we calculate a SCC from DICE (based on 

Nordhaus 2008) of around $9 per ton for a 5-percent discount rate, and a SCC from PAGE 

(based on Hope 2006, 2008) close to $8 per ton for a 4-percent discount rate. Note that these 

14A-27
comparisons are only approximate since the literature generally relies on Ramsey discounting, 

while we have assumed constant discount rates.aa

The SCC estimates from FUND are sensitive to differences in emissions paths but 

relatively insensitive to differences in GDP paths across scenarios, while the reverse is true for 

DICE and PAGE. This likely occurs because of several structural differences among the models. 

Specifically in DICE and PAGE, the fraction of economic output lost due to climate damages 

increases with the level of temperature alone, whereas in FUND the fractional loss also increases 

with the rate of temperature change. Further, in FUND increases in income over time decrease 

vulnerability to climate change (a form of adaptation), whereas this does not occur in DICE and 

PAGE. These structural differences among the models make FUND more sensitive to the path of 

emissions and less sensitive to GDP compared to DICE and PAGE. 

Figure 14A.5.1 shows that IMAGE has the highest GDP in 2100 while MERGE 

Optimistic has the lowest. The ordering of global GDP levels in 2100 directly corresponds to the 

rank ordering of SCC for PAGE and DICE. For FUND, the correspondence is less clear, a result 

that is to be expected given its less direct relationship between its damage function and GDP.

Figure 14A.5.1 Level of Global GDP Across EMF Scenarios

Table 14A.5.2 shows the four selected SCC values in 5-year increments from 2010 to 

2050. Values for 2010, 2020, 2040, and 2050 are calculated by first combining all outputs 

(10,000 estimates per model run) from all scenarios and models for a given discount rate. Values 

for the years in between are calculated using a simple linear interpolation.

aa Nordhaus (2008) runs DICE2007 with ρ = 1.5 and η = 2. The default approach in PAGE2002 (version 1.4epm) 

treats ρ and η as random parameters, specified using a triangular distribution such that the min, mode, and max = 

0.1, 1, and 2 for ρ, and 0.5, 1, and 2 for η, respectively. The FUND default value for η is 1, and Tol generates SCC 

estimates for values of ρ = 0, 1, and 3 in many recent papers (e.g., Anthoff et al. 2009). The path of per-capita 

consumption growth, g, varies over time but is treated deterministically in two of the three models. In DICE, g is 

endogenous. Under Ramsey discounting, as economic growth slows in the future, the large damages from climate 

change that occur far out in the future are discounted at a lower rate than impacts that occur in the nearer term.

0

50

100

150

200

250

300

350

400

450

2000 2010 2020 2030 2050 2100

Global 

GDP

(trillions

of U.S. 

dollars)

IMAGE

MERGE Optimistic

MESSAGE

MiniCAM

3.7 W/m2 radiative forcing

14A-28

 
Table 14A.5.2 Social Cost of CO2, 2010–2050 (2007$)

Discount Rate 5% 3% 2.5% 3%

Year Avg Avg Avg 95th

2010 4.7 21.4 35.1 64.9

2015 5.7 23.8 38.4 72.8

2020 6.8 26.3 41.7 80.7

2025 8.2 29.6 45.9 90.4

2030 9.7 32.8 50.0 100.0

2035 11.2 36.0 54.2 109.7

2040 12.7 39.2 58.4 119.3

2045 14.2 42.1 61.7 127.8

2050 15.7 44.9 65.0 136.2

The SCC increases over time because future emissions are expected to produce larger 

incremental damages as physical and economic systems become more stressed in response to 

greater climatic change. Note that this approach allows us to estimate the growth rate of the SCC 

directly using DICE, PAGE, and FUND rather than assuming a constant annual growth rate as 

was done for the interim estimates (using 3 percent). This helps to ensure that the estimates are 

internally consistent with other modeling assumptions. Table 14A.5.3 illustrates how the growth 

rate for these four SCC estimates varies over time. The full set of annual SCC estimates between 

2010 and 2050 is reported in the Appendix.

Table 14A.5.3 Changes in the Average Annual Growth Rates of SCC Estimates between 

2010 and 2050

Average Annual 

Growth Rate 

5% 3% 2.5% 3.0%

Year Range Avg Avg Avg 95th

2010–2020 3.6 2.1 1.7 2.2

2020–2030 3.7 2.2 1.8 2.2

2030–2040 2.7 1.8 1.6 1.8

2040–2050 2.1 1.4 1.1 1.3

While the SCC estimate grows over time, the future monetized value of emissions 

reductions in each year (the SCC in year t multiplied by the change in emissions in year t) must 

be discounted to the present to determine its total net present value for use in regulatory analysis. 

Damages from future emissions should be discounted at the same rate as that used to calculate 

the SCC estimates themselves to ensure internal consistency—i.e., future damages from climate 

change, whether they result from emissions today or emissions in a later year, should be 

discounted using the same rate. For example, climate damages in 2020 that are calculated using a 

SCC based on a 5-percent discount rate also should be discounted back to the analysis year using 

a 5-percent discount rate.bb 

14A.6 LIMITATIONS OF THE ANALYSIS

As noted, any estimate of the SCC must be taken as provisional and subject to further 

refinement (and possibly significant change) in accordance with evolving scientific, economic, 

and ethical understandings. During the course of our modeling, it became apparent that there are 

bb However, it is possible that other benefits or costs of proposed regulations unrelated to CO2 emissions will be 

discounted at rates that differ from those used to develop the SCC estimates. 

14A-29

 
several areas in particular need of additional exploration and research. These caveats and 

additional observations in the following section are necessary to consider when interpreting and 

applying the SCC estimates.

Incomplete treatment of non-catastrophic damages. The impacts of climate change are 

expected to be widespread, diverse, and heterogeneous. In addition, the exact magnitude of these 

impacts is uncertain because of the inherent complexity of climate processes, the economic 

behavior of current and future populations, and our inability to accurately forecast technological 

change and adaptation. Current IAMs do not assign value to all of the important physical, 

ecological, and economic impacts of climate change recognized in the climate change literature 

(some of which are discussed above) because of lack of precise information on the nature of 

damages and because the science incorporated into these models understandably lags behind the 

most recent research. Our ability to quantify and monetize impacts will undoubtedly improve 

with time. It is also likely that even in future applications, a number of potentially significant 

damage categories will remain non-monetized. (Ocean acidification is one example of a 

potentially large damage from CO2 emissions not quantified by any of the three models. Species 

and wildlife loss is another example that is exceedingly difficult to monetize.) 

Incomplete treatment of potential catastrophic damages. There has been considerable 

recent discussion of the risk of catastrophic impacts and how best to account for extreme 

scenarios, such as the collapse of the Atlantic Meridional Overturning Circulation or the West 

Antarctic Ice Sheet, or large releases of methane from melting permafrost and warming oceans. 

Weitzman (2009) suggests that catastrophic damages are extremely large—so large, in fact, that 

the damages from a low probability, catastrophic event far in the future dominate the effect of 

the discount rate in a present value calculation and result in an infinite willingness-to-pay for 

mitigation today. However, Nordhaus (2009) concluded that the conditions under which 

Weitzman’s results hold “are limited and do not apply to a wide range of potential uncertain 

scenarios.”

Using a simplified IAM, Newbold and Daigneault (2009) confirmed the potential for 

large catastrophe risk premiums but also showed that the aggregate benefit estimates can be 

highly sensitive to the shapes of both the climate sensitivity distribution and the damage function 

at high temperature changes. Pindyck (2009) also used a simplified IAM to examine high-impact 

low-probability risks, using a right-skewed gamma distribution for climate sensitivity as well as 

an uncertain damage coefficient, but in most cases found only a modest risk premium. Given this 

difference in opinion, further research in this area is needed before its practical significance can 

be fully understood and a reasonable approach developed to account for such risks in regulatory 

analysis. (The next section discusses the scientific evidence on catastrophic impacts in greater 

detail.)

Uncertainty in extrapolation of damages to high temperatures. The damage functions in 

these IAMs are typically calibrated by estimating damages at moderate temperature increases 

(e.g., DICE was calibrated at 2.5 °C) and extrapolated to far higher temperatures by assuming 

that damages increase as some power of the temperature change. Hence, estimated damages are 

far more uncertain under more extreme climate change scenarios. 

14A-30
Incomplete treatment of adaptation and technological change. Each of the three 

integrated assessment models used here assumes a certain degree of low- or no-cost adaptation. 

For instance, Tol assumes a great deal of adaptation in FUND, including widespread reliance on 

air conditioning, so much so that the largest single benefit category in FUND is the reduced 

electricity costs from not having to run air conditioning as intensively (NRC 2009). 

Climate change also will increase returns on investment to develop technologies that 

allow individuals to cope with adverse climate conditions, and IAMs to do not adequately 

account for this directed technological change.cc For example, scientists may develop crops that 

are better able to withstand higher and more variable temperatures. Although DICE and FUND 

have both calibrated their agricultural sectors under the assumption that farmers will change land 

use practices in response to climate change (Mastrandrea 2009), they do not take into account 

technological changes that lower the cost of this adaptation over time. On the other hand, the 

calibrations do not account for increases in climate variability, pests, or diseases, which could 

make adaptation more difficult than assumed by the IAMs for a given temperature change.

Hence, models do not adequately account for potential adaptation or technical change that might 

alter the emissions pathway and resulting damages. In this respect, it is difficult to determine 

whether the incomplete treatment of adaptation and technological change in these IAMs under or 

overstate the likely damages.

Risk aversion. A key question unanswered during this interagency process is what to 

assume about relative risk aversion with regard to high-impact outcomes. These calculations do 

not take into account the possibility that individuals may have a higher willingness to pay to 

reduce the likelihood of low-probability, high-impact damages than they do to reduce the 

likelihood of higher-probability but lower-impact damages with the same expected cost. (The 

inclusion of the 95th percentile estimate in the final set of SCC values was largely motivated by 

this concern.) If individuals do show such a higher willingness to pay, a further question is 

whether that fact should be taken into account for regulatory policy. Even if individuals are not 

risk-averse for such scenarios, it is possible that regulatory policy should include a degree of 

risk-aversion.

Assuming a risk-neutral representative agent is consistent with OMB’s Circular A-4, 

which advises that the estimates of benefits and costs used in regulatory analysis are usually 

based on the average or the expected value and that “emphasis on these expected values is 

appropriate as long as society is ‘risk neutral’ with respect to the regulatory alternatives. While 

this may not always be the case, [analysts] should in general assume ‘risk neutrality’ in [their] 

analysis.” 

Nordhaus (2008) points to the need to explore the relationship between risk and income 

in the context of climate change across models and to explore the role of uncertainty regarding 

various parameters in the results. Using FUND, Anthoff et al. (2009) explored the sensitivity of 

the SCC to Ramsey equation parameter assumptions based on observed behavior. They conclude 

that “the assumed rate of risk aversion is at least as important as the assumed rate of time 

preference in determining the social cost of carbon.” Since Circular A-4 allows for a different 

cc However these research dollars will be diverted from whatever their next best use would have been in the absence 

of climate change (so productivity/GDP would have been still higher).

14A-31

 
assumption on risk preference in regulatory analysis if it is adequately justified, we plan to 

continue investigating this issue.

14A.7 A FURTHER DISCUSSION OF CATASTROPHIC IMPACTS AND DAMAGE 

FUNCTIONS

As noted above, the damage functions underlying the three IAMs used to estimate the 

SCC may not capture the economic effects of all possible adverse consequences of climate 

change and may therefore lead to underestimates of the SCC (Mastrandrea 2009). In particular, 

the models’ functional forms may not adequately capture: (1) potentially discontinuous “tipping 

point” behavior in Earth systems; (2) inter-sectoral and inter-regional interactions, including 

global security impacts of high-end warming; and (3) limited near-term substitutability between 

damage to natural systems and increased consumption. 

It is the hope of the interagency group that over time researchers and modelers will work 

to fill these gaps and that the SCC estimates used for regulatory analysis by the Federal 

government will continue to evolve with improvements in modeling. In the meantime, we 

discuss some of the available evidence.

14A.7.1 Extrapolation of Climate Damages to High Levels of Warming

The damage functions in the models are calibrated at moderate levels of warming and 

should therefore be viewed cautiously when extrapolated to the high temperatures found in the 

upper end of the distribution. Recent science suggests that there are a number of potential 

climatic “tipping points” at which the Earth system may exhibit discontinuous behavior with 

potentially severe social and economic consequences (e.g., Lenton et al. 2008, Kriegler et al.

2009). These tipping points include the disruption of the Indian Summer Monsoon, dieback of 

the Amazon Rainforest and boreal forests, collapse of the Greenland Ice Sheet and the West 

Antarctic Ice Sheet, reorganization of the Atlantic Meridional Overturning Circulation, 

strengthening of El Niño-Southern Oscillation, and the release of methane from melting 

permafrost. Many of these tipping points are estimated to have thresholds between about 3 °C 

and 5 °C (Lenton et al. 2008). Probabilities of several of these tipping points were assessed 

through expert elicitation in 2005–2006 by Kriegler et al. (2009); results from this study are 

highlighted in Table 14A.7.1. Ranges of probability are averaged across core experts on each 

topic.

14A-32
Table 14A.7.1 Probabilities of Various Tipping Points from Expert Elicitation

Possible Tipping Points Duration before 

effect is fully realized

years

Additional Warming by 2100

%

0.5–1.5 C 1.5–3.0 C 3–5 C

Reorganization of Atlantic Meridional 

Overturning Circulation

about 100 0–18 6–39 18–67

Greenland Ice Sheet Collapse at least 300 8–39 33–73 67–96

West Antarctic Ice Sheet Collapse at least 300 5–41 10–63 33–88

Dieback of Amazon rainforest about 50 2–46 14–84 41–94

Strengthening of El Niño-Southern 

Oscillation

about 100 1–13 6–32 19–49

Dieback of Boreal Forests about 50 13–43 20–81 34–91

Shift in Indian Summer Monsoon about 1 not formally assessed

Release of Methane from Melting 

Permafrost

less than 100 not formally assessed

As previously mentioned, FUND does not include potentially catastrophic effects. DICE 

assumes a small probability of catastrophic damages that increases with increased warming, but 

the damages from these risks are incorporated as expected values (i.e., ignoring potential risk 

aversion). PAGE models catastrophic impacts in a probabilistic framework (Figure 14A.4.1), so 

the high-end output from PAGE potentially offers the best insight into the SCC if the world were 

to experience catastrophic climate change. For instance, at the 95th percentile and a 3-percent 

discount rate, the SCC estimated by PAGE across the five socio-economic and emission 

trajectories of $113 per ton of CO2 is almost double the value estimated by DICE, $58 per ton in 

2010. We cannot evaluate how well the three models account for catastrophic or noncatastrophic impacts, but this estimate highlights the sensitivity of SCC values in the tails of the 

distribution to the assumptions made about catastrophic impacts. 

PAGE treats the possibility of a catastrophic event probabilistically, while DICE treats it 

deterministically (i.e., by adding the expected value of the damage from a catastrophe to the 

aggregate damage function). In part, this results in different probabilities being assigned to a 

catastrophic event across the two models. For instance, PAGE places a probability near zero on a 

catastrophe at 2.5 °C warming, while DICE assumes a 4 percent probability of a catastrophe at 

2.5 °C. By comparison, Kriegler et al. (2009) estimate a probability of at least 16–36 percent of 

crossing at least one of their primary climatic tipping points in a scenario with temperatures 

about 2–4 °C warmer than pre-Industrial levels in 2100. 

It is important to note that crossing a climatic tipping point will not necessarily lead to an 

economic catastrophe in the sense used in the IAMs. A tipping point is a critical threshold across 

which some aspect of the Earth system starts to shifts into a qualitatively different state (for 

instance, one with dramatically reduced ice sheet volumes and higher sea levels). In the IAMs, a 

catastrophe is a low-probability environmental change with high economic impact.

14A.7.2 Failure to Incorporate Inter-Sectoral and Inter-Regional Interactions

The damage functions do not fully incorporate either inter-sectoral or inter-regional 

interactions. For instance, while damages to the agricultural sector are incorporated, the effects 

of changes in food supply on human health are not fully captured and depend on the modeler’s 

choice of studies used to calibrate the IAM. Likewise, the effects of climate damages in one 

14A-33
region of the world on another region are not included in some of the models (FUND includes 

the effects of migration from sea level rise). These inter-regional interactions, though difficult to 

quantify, are the basis for climate-induced national and economic security concerns (e.g., 

Campbell et al. 2007; U.S. Department of Defense 2010) and are particularly worrisome at 

higher levels of warming. High-end warming scenarios, for instance, project water scarcity 

affecting 4.3–6.9 billion people by 2050, food scarcity affecting about 120 million additional 

people by 2080, and the creation of millions of climate refugees (Easterling et al. 2007; 

Campbell et al. 2007).

14A.7.3 Imperfect Substitutability of Environmental Amenities

Data from the geological record of past climate changes suggests that 6 °C of warming 

may have severe consequences for natural systems. For instance, during the Paleocene-Eocene 

Thermal Maximum about 55.5 million years ago, when the Earth experienced a geologically 

rapid release of carbon associated with an approximately 5 °C increase in global mean 

temperatures, the effects included shifts of about 400–900 miles in the range of plants (Wing 

et al. 2005), and dwarfing of both land mammals (Gingerich 2006) and soil fauna (Smith et al.

2009).

The three IAMs used here assume that it is possible to compensate for the econoic 

consequences of damages to natural systems through increased consumption of non-climate 

goods, a common assumption in many economic models. In the context of climate change, 

however, it is possible that the damages to natural systems could become so great that no 

increase in consumption of non-climate goods would provide complete compensation (Levy 

et al. 2005). For instance, as water supplies become scarcer or ecosystems become more fragile 

and less bio-diverse, the services they provide may become increasingly more costly to replace. 

Uncalibrated attempts to incorporate the imperfect substitutability of such amenities into IAMs 

(Sterner and Persson 2008) indicate that the optimal degree of emissions abatement can be 

considerably greater than is commonly recognized. 

14A.8 CONCLUSION

The interagency group selected four SCC estimates for use in regulatory analyses. For 

2010, these estimates are $4.7, $21.4, $35.1, and $64.9 (2007$). The first three estimates are 

based on the average SCC across models and socio-economic and emissions scenarios at the 5-, 

3-, and 2.5-percent discount rates, respectively. The fourth value is included to represent the 

higher-than-expected impacts from temperature change further out in the tails of the SCC 

distribution. For this purpose, we use the SCC value for the 95th percentile at a 3-percent 

discount rate. The central value is the average SCC across models at the 3-percent discount rate. 

For purposes of capturing the uncertainties involved in regulatory impact analysis, we emphasize 

the importance and value of considering the full range. These SCC estimates also grow over 

time. For instance, the central value increases to $24 per ton of CO2 in 2015 and $26 per ton of 

CO2 in 2020.

We noted a number of limitations to this analysis, including the incomplete way in which 

the integrated assessment models capture catastrophic and non-catastrophic impacts, their 

incomplete treatment of adaptation and technological change, uncertainty in the extrapolation of 

14A-34
damages to high temperatures, and assumptions regarding risk aversion. The limited amount of 

research linking climate impacts to economic damages makes this modeling exercise even more 

difficult. It is the hope of the interagency group that over time researchers and modelers will 

work to fill these gaps and that the SCC estimates used for regulatory analysis by the Federal 

government will continue to evolve with improvements in modeling. 

14A-35
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14A-41
14A.9.1 ANNEX

This Annex provides additional technical information about the non-CO2 emission 

projections used in the modeling and the method for extrapolating emissions forecasts through 

2300, and shows the full distribution of 2010 SCC estimates by model and scenario combination. 

Annual SCC values for the next 40 years are provided in Table 14A.9.1. 

Table 14A.9.1 Annual SCC Values: 2010–2050 (2007$)

Discount Rate 5% 3% 2.5% 3%

Year Avg Avg Avg 95th

2010 4.7 21.4 35.1 64.9

2011 4.9 21.9 35.7 66.5

2012 5.1 22.4 36.4 68.1

2013 5.3 22.8 37.0 69.6

2014 5.5 23.3 37.7 71.2

2015 5.7 23.8 38.4 72.8

2016 5.9 24.3 39.0 74.4

2017 6.1 24.8 39.7 76.0

2018 6.3 25.3 40.4 77.5

2019 6.5 25.8 41.0 79.1

2020 6.8 26.3 41.7 80.7

2021 7.1 27.0 42.5 82.6

2022 7.4 27.6 43.4 84.6

2023 7.7 28.3 44.2 86.5

2024 7.9 28.9 45.0 88.4

2025 8.2 29.6 45.9 90.4

2026 8.5 30.2 46.7 92.3

2027 8.8 30.9 47.5 94.2

2028 9.1 31.5 48.4 96.2

2029 9.4 32.1 49.2 98.1

2030 9.7 32.8 50.0 100.0

2031 10.0 33.4 50.9 102.0

2032 10.3 34.1 51.7 103.9

2033 10.6 34.7 52.5 105.8

2034 10.9 35.4 53.4 107.8

2035 11.2 36.0 54.2 109.7

2036 11.5 36.7 55.0 111.6

2037 11.8 37.3 55.9 113.6

2038 12.1 37.9 56.7 115.5

2039 12.4 38.6 57.5 117.4

2040 12.7 39.2 58.4 119.3

2041 13.0 39.8 59.0 121.0

2042 13.3 40.4 59.7 122.7

2043 13.6 40.9 60.4 124.4

2044 13.9 41.5 61.0 126.1

2045 14.2 42.1 61.7 127.8

2046 14.5 42.6 62.4 129.4

2047 14.8 43.2 63.0 131.1

2048 15.1 43.8 63.7 132.8

2049 15.4 44.4 64.4 134.5

2050 15.7 44.9 65.0 136.2

14A-42
14A.9.2 Other (Non-CO2) Gases

In addition to fossil and industrial CO2 emissions, each EMF scenario provides 

projections of methane (CH4), nitrous oxide (N2O), fluorinated gases, and net land use CO2

emissions to 2100. These assumptions are used in all three IAMs while retaining each model’s 

default radiative forcings (RF) due to other factors (e.g., aerosols and other gases). Specifically, 

to obtain the RF associated with the non-CO2 EMF emissions only, we calculated the RF 

associated with the EMF atmospheric CO2 concentrations and subtracted them from the EMF 

total RF.dd This approach respects the EMF scenarios as much as possible and at the same time 

takes account of those components not included in the EMF projections. Since each model treats 

non-CO2 gases differently (e.g., DICE lumps all other gases into one composite exogenous 

input), this approach was applied slightly differently in each of the models. 

FUND: Rather than relying on RF for these gases, the actual emissions from each 

scenario were used in FUND. The model default trajectories for CH4, N2O, SF6, and the CO2

emissions from land were replaced with the EMF values. 

PAGE: PAGE models CO2, CH4, sulfur hexafluoride (SF6), and aerosols and contains an 

“excess forcing” vector that includes the RF for everything else. To include the EMF values, we 

removed the default CH4 and SF6 factors,

ee decomposed the excess forcing vector, and 

constructed a new excess forcing vector that includes the EMF RF for CH4, N2O, and fluorinated 

gases, as well as the model default values for aerosols and other factors. Net land use CO2

emissions were added to the fossil and industrial CO2 emissions pathway. 

DICE: DICE presents the greatest challenge because all forcing due to factors other than 

industrial CO2 emissions is embedded in an exogenous non-CO2 RF vector. To decompose this 

exogenous forcing path into EMF non-CO2 gases and other gases, we relied on the references in 

DICE2007 to the Intergovernmental Panel on Climate Change’s (IPCC) Fourth Assessment 

Report (AR4) and the discussion of aerosol forecasts in the IPCC’s Third Assessment Report 

(TAR) and in AR4, as explained below. In DICE2007, Nordhaus assumes that exogenous forcing 

from all non-CO2 sources is -0.06 W/m2 in 2005, as reported in AR4, and increases linearly to 

0.3 W/m2 in 2105, based on GISS projections, and then stays constant after that time.

According to AR4, the RF in 2005 from CH4, N20, and halocarbons (approximately 

similar to the F-gases in the EMF-22 scenarios) was 0.48 + 0.16 + 0.34 = 0.98 W/m2 and RF 

from total aerosols was -1.2 W/m2. Thus, the -.06 W/m2non-CO2 forcing in DICE can be 

decomposed into: 0.98 W/m2 due to the EMF non-CO2 gases, -1.2 W/m2 due to aerosols, and the 

remainder, 0.16 W/m2

, due to other residual forcing. 

dd Note EMF did not provide CO2 concentrations for the IMAGE reference scenario. Thus, for this scenario, we fed 

the fossil, industrial and land CO2 emissions into MAGICC (considered a “neutral arbiter” model, which is tuned to 

emulate the major global climate models) and the resulting CO2 concentrations were used. Note also that MERGE 

assumes a neutral biosphere so net land CO2 emissions are set to zero for all years for the MERGE Optimistic 

reference scenario, and for the MERGE component of the average 550 scenario (i.e., we add up the land use 

emissions from the other three models and divide by 4). ee Both the model default CH4 emissions and the initial atmospheric CH4 is set to zero to avoid double counting the 

effect of past CH4 emissions.

14A-43

 
For subsequent years, we calculated the DICE default RF from aerosols and other nonCO2 gases based on the following two assumptions:

(1) RF from aerosols declines linearly from 2005 to 2100 at the rate projected by the TAR 

and then stays constant thereafter, and 

(2) With respect to RF from non-CO2 gases not included in the EMF-22 scenarios, the share 

of non-aerosol RF matches the share implicit in the AR4 summary statistics cited above 

and remains constant over time. 

Assumption (1) means that the RF from aerosols in 2100 equals 66 percent of that in 

2000, which is the fraction of the TAR projection of total RF from aerosols (including sulfates, 

black carbon, and organic carbon) in 2100 vs. 2000 under the A1B SRES emissions scenario. 

Since the SRES marker scenarios were not updated for the AR4, the TAR provides the most 

recent IPCC projection of aerosol forcing. We rely on the A1B projection from the TAR because 

it provides one of the lower aerosol forecasts among the SRES marker scenarios and is more 

consistent with the AR4 discussion of the post-SRES literature on aerosols: 

Aerosols have a net cooling effect and the representation of aerosol and aerosol precursor 

emissions, including sulphur dioxide, black carbon and organic carbon, has improved in the 

post-SRES scenarios. Generally, these emissions are projected to be lower than reported in 

SRES. {WGIII 3.2, TS.3, SPM}.ff

Assuming a simple linear decline in aerosols from 2000 to 2100 also is more consistent 

with the recent literature on these emissions. For example, Figure 14A.9.1 shows that the sulfur 

dioxide emissions peak over the short-term of some SRES scenarios above the upper bound 

estimates of the more recent scenarios.gg Recent scenarios project sulfur emissions to peak earlier 

and at lower levels compared to the SRES in part because of new information about present and 

planned sulfur legislation in some developing countries, such as India and China.hh The lower 

bound projections of the recent literature have also shifted downward slightly compared to the 

SRES scenario (IPCC 2007).

With these assumptions, the DICE aerosol forcing changes from -1.2 in 2005 to -0.792 in 

2105 W/m2

; forcing due to other non-CO2 gases not included in the EMF scenarios declines from 

0.160 to 0.153 W/m2

. 

ff AR4 Synthesis Report, p. 44, http://www.ipcc.ch/pdf/assessment-report/ar4/syr/ar4_syr.pdf gg See Smith, S.J., R. Andres, E. Conception, and J. Lurz. 2004. “Historical sulfur dioxide emissions, 1850-2000: 

methods and results.” Joint Global Research Institute, College Park, 14 pp. hh See Carmichael, G., D. Streets, G. Calori, M. Amann, M. Jacobson, J. Hansen, and H. Ueda. 2002. “Changing 

trends in sulphur emissions in Asia: implications for acid deposition, air pollution, and climate.” Environmental 

Science and Technology 36(22):4707- 4713; Streets, D., K. Jiang, X. Hu, J. Sinton, X.-Q. Zhang, D. Xu, M. 

Jacobson, and J. Hansen. 2001. “Recent reductions in China’s greenhouse gas emissions.” Science 294(5548):1835–

1837.

14A-44

 
Figure 14A.9.1 Sulphur Dioxide Emission Scenarios

Notes: Thick colored lines depict the four SRES marker scenarios and black dashed lines show the median, 5th

and 95th percentile of the frequency distribution for the full ensemble of 40 SRES scenarios. The blue area (and 

the thin dashed lines in blue) illustrates individual scenarios and the range of Smith et al. (2004). Dotted lines 

indicate the minimum and maximum of SO2 emissions scenarios developed pre-SRES.

Source: IPCC (2007), AR4 WGIII 3.2, http://www.ipcc.ch/publications_and_data/ar4/wg3/en/ch3-ens3-2-2-

4.html.

Although other approaches to decomposing the DICE exogenous forcing vector are 

possible, initial sensitivity analysis suggests that the differences among reasonable alternative 

approaches are likely to be minor. For example, adjusting the TAR aerosol projection above to 

assume that aerosols will be maintained at 2000 levels through 2100 reduces average SCC values 

(for 2010) by approximately 3 percent (or less than $2); assuming all aerosols are phased out by 

2100 increases average 2010 SCC values by 6–7 percent (or $0.50–$3), depending on the 

discount rate. These differences increase slightly for SCC values in later years but are still well 

within 10 percent of each other as far out as 2050. 

Finally, as in PAGE, the EMF net land use CO2 emissions are added to the fossil and 

industrial CO2 emissions pathway. 

14A.9.3 Extrapolating Emissions Projections to 2300

To run each model through 2300 requires assumptions about GDP, population, 

greenhouse gas emissions, and radiative forcing trajectories after 2100, the last year for which 

these projections are available from the EMF-22 models. These inputs were extrapolated from 

2100 to 2300 as follows:

1. Population growth rate declines linearly, reaching zero in 2200.

2. GDP/per capita growth rate declines linearly, reaching zero in 2300.

3. The decline in the fossil and industrial carbon intensity (CO2/GDP) growth rate 

over 2090-2100 is maintained from 2100 through 2300.

4. Net land use CO2 emissions decline linearly, reaching zero in 2200.

14A-45
5. Non-CO2 radiative forcing remains constant after 2100.

Long run stabilization of GDP per capita was viewed as a more realistic simplifying 

assumption than a linear or exponential extrapolation of the pre-2100 economic growth rate of 

each EMF scenario. This is based on the idea that increasing scarcity of natural resources and the 

degradation of environmental sinks available for assimilating pollution from economic 

production activities may eventually overtake the rate of technological progress. Thus, the 

overall rate of economic growth may slow over the very long run. The interagency group also 

considered allowing an exponential decline in the growth rate of GDP per capita. However, since 

this would require an additional assumption about how close to zero the growth rate would get 

by 2300, the group opted for the simpler and more transparent linear extrapolation to zero by 

2300. 

The population growth rate is also assumed to decline linearly, reaching zero by 2200. 

This assumption is reasonably consistent with the United Nations long run population forecast, 

which estimates global population to be fairly stable after 2150 in the medium scenario 

(UN 2004).ii The resulting range of EMF population trajectories (Table 14A.9.2) also encompass 

the UN medium scenario forecasts through 2300 – global population of 8.5 billion by 2200, and 

9 billion by 2300. 

Maintaining the decline in the 2090–2100 carbon intensity growth rate (i.e., CO2 per 

dollar of GDP) through 2300 assumes that technological improvements and innovations in the 

areas of energy efficiency and other carbon reducing technologies (possibly including currently 

unavailable methods) will continue to proceed at roughly the same pace that is projected to occur 

towards the end of the forecast period for each EMF scenario. This assumption implies that total 

cumulative emissions in 2300 will be between 5,000 and 12,000 GtC, which is within the range 

of the total potential global carbon stock estimated in the literature.

Net land use CO2 emissions are expected to stabilize in the long run, so in the absence of 

any post 2100 projections, the group assumed a linear decline to zero by 2200. Given no a priori 

reasons for assuming a long run increase or decline in non-CO2 radiative forcing, it is assumed to 

remain at the 2100 levels for each EMF scenario through 2300. 

Figure 14A.9.2 through Figure 14A.9.8 show the paths of global population, GDP, fossil 

and industrial CO2 emissions, net land CO2 emissions, non-CO2 radiative forcing, and CO2

intensity (fossil and industrial CO2 emissions/GDP) resulting from these assumptions. 

ii United Nations. 2004. World Population to 2300.

http://www.un.org/esa/population/publications/longrange2/worldpop2300final.pdf. 

14A-46

 
Figure 14A.9.2 Global Population, 2000–2300 (post-2100 extrapolations assume the 

population growth rate changes linearly to reach a zero growth rate by 2200)

Note: In the fifth scenario, 2000–2100 population is equal to the average of the population under the 550 ppm 

CO2e, full-participation, not-to-exceed scenarios considered by each of the four models. 

Figure 14A.9.3 World GDP, 2000-2300 (post-2100 extrapolations assume GDP per capita 

growth declines linearly, reaching zero in 2300)

Note: In the fifth scenario, 2000–2100 GDP is equal to the average of the GDP under the 550 ppm CO2e, fullparticipation, not-to-exceed scenarios considered by each of the four models. 

5.5

6.5

7.5

8.5

9.5

10.5

11.5

2000 2050 2100 2150 2200 2250 2300

IMAGE

MERGE

MESSAGE

MiniCAM

5th scenario

Extrapolations

Global Population [billion]

Year

0

500

1000

1500

2000

2000 2050 2100 2150 2200 2250 2300

IMAGE

MERGE

MESSAGE

MiniCAM

5th scenario

Extrapolations

Year

World GDP [$trillion]

14A-47
Figure 14A.9.4 Global Fossil and Industrial CO2 Emissions, 2000-2300 (post-2100 

extrapolations assume growth rate of CO2 intensity (CO2/GDP) over 2090–2100 is 

maintained through 2300)

Note: In the fifth scenario, 2000–2100 emissions are equal to the average of the emissions under the 550 ppm 

CO2e, full-participation, not-to-exceed scenarios considered by each of the four models. 

Figure 14A.9.5 Global Net Land Use CO2 Emissions, 2000–2300 (post-2100 extrapolations 

assume emissions decline linearly, reaching zero in 2200)

jj

Note: In the fifth scenario, 2000–2100 emissions are equal to the average of the emissions under the 550 ppm 

CO2e, full-participation, not-to-exceed scenarios considered by each of the four models. 

jj MERGE assumes a neutral biosphere so net land CO2 emissions are set to zero for all years for the MERGE 

Optimistic reference scenario, and for the MERGE component of the average 550 scenario (i.e., we add up the land 

use emissions from the other three models and divide by 4).

0

50

100

150

200

2000 2050 2100 2150 2200 2250 2300

IMAGE

MERGE

MESSAGE

MiniCAM

5th scenario

Extrapolations

Fossil&Industrial CO2 Emissions [GtCO2/decade]

Year

-3.00

-2.00

-1.00

0.00

1.00

2.00

3.00

4.00

2000 2050 2100 2150 2200 2250 2300

IMAGE

MERGE

MESSAGE

MiniCAM

5th scneario

Extrapolations

Net land CO2 emissions [GtCO2/yr]

Year

z

14A-48

 
Figure 14A.9.6 Global Non-CO2 Radiative Forcing, 2000–2300 (post-2100 extrapolations 

assume constant non-CO2 radiative forcing after 2100)

Note: In the fifth scenario, 2000–2100 emissions are equal to the average of the emissions under the 550 ppm 

CO2e, full-participation, not-to-exceed scenarios considered by each of the four models. 

Figure 14A.9.7 Global CO2 Intensity (fossil & industrial CO2 emissions/GDP), 2000–2300 

(post-2100 extrapolations assume decline in CO2/GDP growth rate over 2090–2100 is 

maintained through 2300)

Note: In the fifth scenario, 2000–2100 emissions are equal to the average of the emissions under the 550 ppm 

CO2e, full-participation, not-to-exceed scenarios considered by each of the four models. 

0.00

0.75

1.50

2.25

3.00

3.75

2000 2050 2100 2150 2200 2250 2300

IMAGE

MERGE

MESSAGE

MiniCAM

5th scenario

Extrapolations

nonCO2 RF [W/m^2]

Year

0

0

0

0

0

1

1

1

1

2000 2050 2100 2150 2200 2250 2300

IMAGE

MERGE

MESSAGE

MiniCAM

5th scenario

Extrapolations

Fossil & Industrial CO2 intensity [GtCO2/trillion $]

Year

14A-49
Table 14A.9.2 2010 Global SCC Estimates at 2.5-Percent Discount Rate (2007$/ton CO2)

Percentile 1st 5th 10th 25th 50th Avg 75th 90th 95th 99th

Scenario PAGE

IMAGE 3.3 5.9 8.1 13.9 28.8 65.5 68.2 147.9 239.6 563.8

MERGE optimistic 1.9 3.2 4.3 7.2 14.6 34.6 36.2 79.8 124.8 288.3

Message 2.4 4.3 5.8 9.8 20.3 49.2 50.7 114.9 181.7 428.4

MiniCAM base 2.7 4.6 6.4 11.2 22.8 54.7 55.7 120.5 195.3 482.3

5th scenario 2.0 3.5 4.7 8.1 16.3 42.9 41.5 103.9 176.3 371.9

Scenario DICE

IMAGE 16.4 21.4 25 33.3 46.8 54.2 69.7 96.3 111.1 130.0

MERGE optimistic 9.7 12.6 14.9 19.7 27.9 31.6 40.7 54.5 63.5 73.3

Message 13.5 17.2 20.1 27 38.5 43.5 55.1 75.8 87.9 103.0

MiniCAM base 13.1 16.7 19.8 26.7 38.6 44.4 56.8 79.5 92.8 109.3

5th scenario 10.8 14 16.7 22.2 32 37.4 47.7 67.8 80.2 96.8

Scenario FUND

IMAGE -33.1 -18.9 -13.3 -5.5 4.1 19.3 18.7 43.5 67.1 150.7

MERGE optimistic -33.1 -14.8 -10 -3 5.9 14.8 20.4 43.9 65.4 132.9

Message -32.5 -19.8 -14.6 -7.2 1.5 8.8 13.8 33.7 52.3 119.2

MiniCAM base -31.0 -15.9 -10.7 -3.4 6 22.2 21 46.4 70.4 152.9

5th scenario -32.2 -21.6 -16.7 -9.7 -2.3 3 6.7 20.5 34.2 96.8

Table 14A.9.3 2010 Global SCC Estimates at 3-Percent Discount Rate (2007$/ton CO2)

Percentile 1st 5th 10th 25th 50th Avg 75th 90th 95th 99th

Scenario PAGE

IMAGE 2.0 3.5 4.8 8.1 16.5 39.5 41.6 90.3 142.4 327.4

MERGE optimistic 1.2 2.1 2.8 4.6 9.3 22.3 22.8 51.3 82.4 190.0

Message 1.6 2.7 3.6 6.2 12.5 30.3 31 71.4 115.6 263.0

MiniCAM base 1.7 2.8 3.8 6.5 13.2 31.8 32.4 72.6 115.4 287.0

5th scenario 1.3 2.3 3.1 5 9.6 25.4 23.6 62.1 104.7 222.5

Scenario DICE

IMAGE 11.0 14.5 17.2 22.8 31.6 35.8 45.4 61.9 70.8 82.1

MERGE optimistic 7.1 9.2 10.8 14.3 19.9 22 27.9 36.9 42.1 48.8

Message 9.7 12.5 14.7 19 26.6 29.8 37.8 51.1 58.6 67.4

MiniCAM base 8.8 11.5 13.6 18 25.2 28.8 36.9 50.4 57.9 67.8

5th scenario 7.9 10.1 11.8 15.6 21.6 24.9 31.8 43.7 50.8 60.6

Scenario FUND

IMAGE -25.2 -15.3 -11.2 -5.6 0.9 8.2 10.4 25.4 39.7 90.3

MERGE optimistic -24.0 -12.4 -8.7 -3.6 2.6 8 12.2 27 41.3 85.3

Message -25.3 -16.2 -12.2 -6.8 -0.5 3.6 7.7 20.1 32.1 72.5

MiniCAM base -23.1 -12.9 -9.3 -4 2.4 10.2 12.2 27.7 42.6 93.0

5th scenario -24.1 -16.6 -13.2 -8.3 -3 -0.2 2.9 11.2 19.4 53.6

14A-50
Table 14A.9.4 2010 Global SCC Estimates at 5-Percent Discount Rate (2007$/ton CO2)

Percentile 1st 5th 10th 25th 50th Avg 75th 90th 95th 99th

Scenario PAGE

IMAGE 0.5 0.8 1.1 1.8 3.5 8.3 8.5 19.5 31.4 67.2

MERGE optimistic 0.3 0.5 0.7 1.2 2.3 5.2 5.4 12.3 19.5 42.4

Message 0.4 0.7 0.9 1.6 3 7.2 7.2 17 28.2 60.8

MiniCAM base 0.3 0.6 0.8 1.4 2.7 6.4 6.6 15.9 24.9 52.6

5th scenario 0.3 0.6 0.8 1.3 2.3 5.5 5 12.9 22 48.7

Scenario DICE

IMAGE 4.2 5.4 6.2 7.6 10 10.8 13.4 16.8 18.7 21.1

MERGE optimistic 2.9 3.7 4.2 5.3 7 7.5 9.3 11.7 12.9 14.4

Message 3.9 4.9 5.5 7 9.2 9.8 12.2 15.4 17.1 18.8

MiniCAM base 3.4 4.2 4.7 6 7.9 8.6 10.7 13.5 15.1 16.9

5th scenario 3.2 4 4.6 5.7 7.6 8.2 10.2 12.8 14.3 16.0

Scenario FUND

IMAGE -11.7 -8.4 -6.9 -4.6 -2.2 -1.3 0.7 4.1 7.4 17.4

MERGE optimistic -10.6 -7.1 -5.6 -3.6 -1.3 -0.3 1.6 5.4 9.1 19.0

Message -12.2 -8.9 -7.3 -4.9 -2.5 -1.9 0.3 3.5 6.5 15.6

MiniCAM base -10.4 -7.2 -5.8 -3.8 -1.5 -0.6 1.3 4.8 8.2 18.0

5th scenario -10.9 -8.3 -7 -5 -2.9 -2.7 -0.8 1.4 3.2 9.2

Figure 14A.9.8 Histogram of Global SCC Estimates in 2010 (2007$/ton CO2), by Discount 

Rate*

* The distribution of SCC values ranges from -$5,192 to $66,116, but the X-axis has been truncated at 

approximately the 1st and 99th percentiles to better show the data.

0

2000

4000

6000

8000

10000

12000

14000

16000

-25-12

1

14274053667992

105118131144157170183196209222235248261274

All 2010, 5.0% All 2010, 3.0% All 2010, 2.5%

14A-51
Table 14A.9.5 Additional Summary Statistics of 2010 Global SCC Estimates 

Discount 

Rate

5% 3% 2.5%

Scenario Mean Variance Skewness Kurtosis Mean Variance Skewness Kurtosis Mean Variance Skewness Kurtosis

DICE 9.0 13.1 0.8 0.2 28.3 209.8 1.1 0.9 42.2 534.9 1.2 1.1

PAGE 6.5 136.0 6.3 72.4 29.8 3,383.7 8.6 151.0 49.3 9,546.0 8.7 143.8

FUND -1.3 70.1 28.2 1,479.0 6.0 16,382.5 128.0 18,976.5 13.6 150,732.6 149.0 23,558.3

14A-52
APPENDIX 14B. TECHNICAL UPDATE OF THE SOCIAL COST OF CARBON FOR 

REGULATORY IMPACT ANALYSIS ­ UNDER EXECUTIVE ORDER 12866

TABLE OF CONTENTS

14B.1 EXECUTIVE SUMMARY.......................................................................................14B-1

14B.2 PURPOSE .................................................................................................................14B-2

14B.3 SUMMARY OF MODEL UPDATES......................................................................14B-3

14B.3.1 DICE.............................................................................................................. 14B-4

14B.3.2 FUND............................................................................................................ 14B-6

14B.3.3 PAGE ............................................................................................................ 14B-8

14B.4 REVISED SCC ESTIMATES ................................................................................14B-11

14B.5 OTHER MODEL LIMITATIONS AND RESEARCH GAPS...............................14B-13

REFERENCES ......................................................................................................................14B-15

APPENDIX A........................................................................................................................14B-17

APPENDIX B........................................................................................................................14B-21

LIST OF TABLES

Table 14B.4.1 Revised Social Cost of CO2, 2010–2050 (in 2007 dollars per metric ton of 

CO2) .................................................................................................................14B-2

Table 14B.4.2 Summary of Key Model Revisions Relevant to the Interagency SCC ............14B-4

Table 14B.4.1 Revised Social Cost of CO2, 2010–2050 (in 2007 dollars per metric ton of 

CO2) ...............................................................................................................14B-12

Table 14B.4.2 Average Annual Growth Rates of SCC Estimates between 2010 and 2050..14B-13

LIST OF FIGURES

Figure 14B.4.1 Distribution of SCC Estimates for 2020 (in 2007$ per metric ton CO2) .....14B-12

14B-i
APPENDIX 14B. TECHNICAL UPDATE OF THE SOCIAL COST OF CARBON FOR 

REGULATORY IMPACT ANALYSIS ­ UNDER EXECUTIVE ORDER 12866 

Interagency Working Group on Social Cost of Carbon, United States Government

With participation by

Council of Economic Advisers

Council on Environmental Quality

Department of Agriculture

Department of Commerce

Department of Energy

Department of Transportation

Domestic Policy Council

Environmental Protection Agency

National Economic Council

Office of Management and Budget

Office of Science and Technology Policy

Department of the Treasury

May 2013

Revised November 2013

See Appendix B for Details on Revision

14B.1 EXECUTIVE SUMMARY

Under Executive Order 12866, agencies are required, to the extent permitted by law, “to 

assess both the costs and the benefits of the intended regulation and, recognizing that some costs 

and benefits are difficult to quantify, propose or adopt a regulation only upon a reasoned 

determination that the benefits of the intended regulation justify its costs.” The purpose of the 

“social cost of carbon” (SCC) estimates presented here is to allow agencies to incorporate the 

social benefits of reducing carbon dioxide (CO2) emissions into cost-benefit analyses of 

regulatory actions that impact cumulative global emissions. The SCC is an estimate of the 

monetized damages associated with an incremental increase in carbon emissions in a given year. 

It is intended to include (but is not limited to) changes in net agricultural productivity, human 

health, property damages from increased flood risk, and the value of ecosystem services due to 

climate change.

The interagency process that developed the original U.S. government’s SCC estimates is 

described in the 2010 interagency technical support document (TSD) (Interagency Working 

Group on Social Cost of Carbon 2010). Through that process the interagency group selected four 

SCC values for use in regulatory analyses. Three values are based on the average SCC from 

three integrated assessment models (IAMs), at discount rates of 2.5, 3, and 5 percent. The fourth 

value, which represents the 95th percentile SCC estimate across all three models at a 3-percent 

discount rate, is included to represent higher-than-expected impacts from temperature change 

further out in the tails of the SCC distribution.

14B-1
While acknowledging the continued limitations of the approach taken by the interagency 

group in 2010, this document provides an update of the SCC estimates based on new versions of 

each IAM (DICE, PAGE, and FUND). It does not revisit other interagency modeling decisions 

(e.g., with regard to the discount rate, reference case socioeconomic and emission scenarios, or

equilibrium climate sensitivity). Improvements in the way damages are modeled are confined to 

those that have been incorporated into the latest versions of the models by the developers 

themselves in the peer-reviewed literature.

The SCC estimates using the updated versions of the models are higher than those 

reported in the 2010 TSD. By way of comparison, the four 2020 SCC estimates reported in the 

2010 TSD were $7, $26, $42 and $81 (2007$). The corresponding four updated SCC estimates 

for 2020 are $12, $43, $64, and $128 (2007$). The model updates that are relevant to the SCC 

estimates include: an explicit representation of sea level rise damages in the DICE and PAGE 

models; updated adaptation assumptions, revisions to ensure damages are constrained by GDP, 

updated regional scaling of damages, and a revised treatment of potentially abrupt shifts in 

climate damages in the PAGE model; an updated carbon cycle in the DICE model; and updated 

damage functions for sea level rise impacts, the agricultural sector, and reduced space heating 

requirements, as well as changes to the transient response of temperature to the buildup of GHG 

concentrations and the inclusion of indirect effects of methane emissions in the FUND model.

The SCC estimates vary by year, and the following table summarizes the revised SCC estimates 

from 2010 through 2050.

Table 14B.4.1 Revised Social Cost of CO2, 2010–2050 (in 2007 dollars per metric ton of 

CO2)

Discount Rate 5.0% 3.0% 2.5% 3.0%

Year Avg Avg Avg 95th

2010 11 32 51 89

2015 12 37 57 109

2020 12 43 64 128

2025 14 47 69 143

2030 16 52 75 159

2035 19 56 80 175

2040 21 61 86 191

2045 24 66 92 206

2050 26 71 97 220

14B.2 PURPOSE

The purpose of this document is to update the schedule of social cost of carbon (SCC) 

estimates from the 2010 interagency technical support document (TSD) (Interagency Working 

Group on Social Cost of Carbon 2010).

a E.O. 13563 commits the Administration to regulatory 

a In this document, we present all values of the SCC as the cost per metric ton of CO2 emissions. Alternatively, one 

could report the SCC as the cost per metric ton of carbon emissions. The multiplier for translating between mass of 

CO2 and the mass of carbon is 3.67 (the molecular weight of CO2 divided by the molecular weight of carbon = 44/12 

= 3.67).

14B-2

 
decision making “based on the best available science.”b Additionally, the interagency group 

recommended in 2010 that the SCC estimates be revisited on a regular basis or as model updates 

that reflect the growing body of scientific and economic knowledge become available.

c New 

versions of the three integrated assessment models used by the U.S. government to estimate the 

SCC (DICE, FUND, and PAGE), are now available and have been published in the peer 

reviewed literature. While acknowledging the continued limitations of the approach taken by the 

interagency group in 2010 (documented in the original 2010 TSD), this document provides an 

update of the SCC estimates based on the latest peer-reviewed version of the models, replacing 

model versions that were developed up to ten years ago in a rapidly evolving field. It does not 

revisit other assumptions with regard to the discount rate, reference case socioeconomic and 

emission scenarios, or equilibrium climate sensitivity. Improvements in the way damages are 

modeled are confined to those that have been incorporated into the latest versions of the models

by the developers themselves in the peer-reviewed literature. The agencies participating in the 

interagency working group continue to investigate potential improvements to the way in which 

economic damages associated with changes in CO2 emissions are quantified. 

Section 14B.3 summarizes the major updates relevant to SCC estimation that are 

contained in the new versions of the integrated assessment models released since the 2010 

interagency report. 14B.4 presents the updated schedule of SCC estimates for 2010–2050 based 

on these versions of the models. Section 14B.5 provides a discussion of other model limitations 

and research gaps.

14B.3 SUMMARY OF MODEL UPDATES

This section briefly summarizes changes to the most recent versions of the three 

integrated assessment models (IAMs) used by the interagency group in 2010. We focus on 

describing those model updates that are relevant to estimating the social cost of carbon, as 

summarized in Table 14B.4.2. For example, both the DICE and PAGE models now include an 

explicit representation of sea level rise damages. Other revisions to PAGE include: updated 

adaptation assumptions, revisions to ensure damages are constrained by GDP, updated regional 

scaling of damages, and a revised treatment of potentially abrupt shifts in climate damages. The 

DICE model’s simple carbon cycle has been updated to be more consistent with a more complex

climate model. The FUND model includes updated damage functions for sea level rise impacts, 

the agricultural sector, and reduced space heating requirements, as well as changes to the 

transient response of temperature to the buildup of GHG concentrations and the inclusion of 

indirect effects of methane emissions. Changes made to parts of the models that are superseded 

by the interagency working group’s modeling assumptions – regarding equilibrium climate 

sensitivity, discounting, and socioeconomic variables – are not discussed here but can be found 

in the references provided in each section below.

b http://www.whitehouse.gov/sites/default/files/omb/inforeg/eo12866/eo13563_01182011.pdf

c See p. 1, 3, 4, 29, and 33 (Interagency Working Group on Social Cost of Carbon 2010).

14B-3

 
Table 14B.4.1 Summary of Key Model Revisions Relevant to the Interagency SCC

IAM

Version used in 

2010 Interagency 

Analysis

New 

Version Key changes relevant to interagency SCC

DICE 2007 2010 Updated calibration of the carbon cycle model and 

explicit representation of sea level rise (SLR) and 

associated damages. 

FUND 3.5 

(2009) 

3.8 

(2012) 

Updated damage functions for space heating, SLR, 

agricultural impacts, changes to transient response of 

temperature to buildup of GHG concentrations, and 

inclusion of indirect climate effects of methane. 

PAGE 2002 2009 Explicit representation of SLR damages, revisions to 

damage function to ensure damages do not exceed 

100% of GDP, change in regional scaling of damages, 

revised treatment of potential abrupt damages, and 

updated adaptation assumptions. 

14B.3.1 DICE

DICE 2010 includes a number of changes over the previous 2007 version used in the 

2010 interagency report. The model changes that are relevant for the SCC estimates developed 

by the interagency working group include: 1) updated parameter values for the carbon cycle 

model, 2) an explicit representation of sea level dynamics, and 3) a re-calibrated damage 

function that includes an explicit representation of economic damages from sea level rise. 

Changes were also made to other parts of the DICE model—including the equilibrium climate 

sensitivity parameter, the rate of change of total factor productivity, and the elasticity of the 

marginal utility of consumption—but these components of DICE are superseded by the 

interagency working group’s assumptions and so will not be discussed here. More details on 

DICE2007 can be found in Nordhaus (2008) and on DICE2010 in Nordhaus (2010). The 

DICE2010 model and documentation is also available for download from the homepage of 

William Nordhaus.

Carbon Cycle Parameters

DICE uses a three-box model of carbon stocks and flows to represent the accumulation 

and transfer of carbon among the atmosphere, the shallow ocean and terrestrial biosphere, and 

the deep ocean. These parameters are “calibrated to match the carbon cycle in the Model for the 

Assessment of Greenhouse Gas Induced Climate Change (MAGICC)” (Nordhaus 2008 p 44).d

Carbon cycle transfer coefficient values in DICE2010 are based on re-calibration of the model to 

match the newer 2009 version of MAGICC (Nordhaus 2010 p 2). For example, in DICE2010, in 

each decade, 12 percent of the carbon in the atmosphere is transferred to the shallow ocean, 4.7 

percent of the carbon in the shallow ocean is transferred to the atmosphere, 94.8 percent remains 

in the shallow ocean, and 0.5 percent is transferred to the deep ocean. For comparison, in DICE 

2007, 18.9 percent of the carbon in the atmosphere is transferred to the shallow ocean each 

d MAGICC is a simple climate model initially developed by the U.S. National Center for Atmospheric Research that 

has been used heavily by the Intergovernmental Panel on Climate Change (IPCC) to emulate projections from more 

sophisticated state of the art earth system simulation models (Randall et al. 2007).

14B-4

 
decade, 9.7 percent of the carbon in the shallow ocean is transferred to the atmosphere, 85.3 

percent remains in the shallow ocean, and 5 percent is transferred to the deep ocean.

The implication of these changes for DICE2010 is in general a weakening of the ocean as 

a carbon sink and therefore a higher concentration of carbon in the atmosphere than in 

DICE2007, for a given path of emissions. All else equal, these changes will generally increase 

the level of warming and therefore the SCC estimates in DICE2010 relative to those from 

DICE2007.

Sea Level Dynamics

A new feature of DICE2010 is an explicit representation of the dynamics of the global 

average sea level anomaly to be used in the updated damage function (discussed below). This 

section contains a brief description of the sea level rise (SLR) module; a more detailed 

description can be found on the model developer’s website.e The average global sea level 

anomaly is modeled as the sum of four terms that represent contributions from: 1) thermal 

expansion of the oceans, 2) melting of glaciers and small ice caps, 3) melting of the Greenland 

ice sheet, and 4) melting of the Antarctic ice sheet. 

The parameters of the four components of the SLR module are calibrated to match 

consensus results from the IPCC’s Fourth Assessment Report (AR4).f The rise in sea level from 

thermal expansion in each time period (decade) is 2 percent of the difference between the sea 

level in the previous period and the long run equilibrium sea level, which is 0.5 meters per 

degree Celsius (°C) above the average global temperature in 1900. The rise in sea level from the 

melting of glaciers and small ice caps occurs at a rate of 0.008 meters per decade per °C above 

the average global temperature in 1900. 

The contribution to sea level rise from melting of the Greenland ice sheet is more 

complex. The equilibrium contribution to SLR is 0 meters for temperature anomalies less than 

1 oC and increases linearly from 0 meters to a maximum of 7.3 meters for temperature anomalies 

between 1 o

C and 3.5 °C. The contribution to SLR in each period is proportional to the difference 

between the previous period’s sea level anomaly and the equilibrium sea level anomaly, where 

the constant of proportionality increases with the temperature anomaly in the current period.

The contribution to SLR from the melting of the Antarctic ice sheet is -0.001 meters per decade 

when the temperature anomaly is below 3 °C and increases linearly between 3 °C and 6 °C to a 

maximum rate of 0.025 meters per decade at a temperature anomaly of 6 °C.

Re-calibrated Damage Function

Economic damages from climate change in the DICE model are represented by a 

fractional loss of gross economic output in each period. A portion of the remaining economic 

output in each period (net of climate change damages) is consumed and the remainder is invested 

in the physical capital stock to support future economic production, so each period’s climate 

damages will reduce consumption in that period and in all future periods due to the lost 

e Documentation on the new sea level rise module of DICE is available on William Nordhaus’ website at: 

http://nordhaus.econ.yale.edu/documents/SLR_021910.pdf. f For a review of post-IPCC AR4 research on sea level rise, see Nicholls et al. (2011) and NAS (2011). 

14B-5

 
investment. The fraction of output in each period that is lost due to climate change impacts is 

represented as one minus a fraction, which is one divided by a quadratic function of the 

temperature anomaly, producing a sigmoid (“S”-shaped) function.g The loss function in 

DICE2010 has been expanded by adding a quadratic function of SLR to the quadratic function of 

temperature. In DICE2010 the temperature anomaly coefficients have been recalibrated to avoid 

double-counting damages from sea level rise that were implicitly included in these parameters in 

DICE2007. 

The aggregate damages in DICE2010 are illustrated by Nordhaus (2010 p 3), who notes 

that “...damages in the uncontrolled (baseline) [i.e., reference] case ... in 2095 are $12 trillion, 

or 2.8 percent of global output, for a global temperature increase of 3.4 oC above 1900 levels.”

This compares to a loss of 3.2 percent of global output at 3.4 oC in DICE2007. However, in 

DICE2010, annual damages are lower in most of the early periods of the modeling horizon but 

higher in later periods than would be calculated using the DICE2007 damage function. 

Specifically, the percent difference between damages in the base run of DICE2010 and those that 

would be calculated using the DICE2007 damage function starts at +7 percent in 2005, decreases 

to a low of -14 percent in 2065, then continuously increases to +20 percent by 2300 (the end of 

the interagency analysis time horizon), and to +160 percent by the end of the model time horizon 

in 2595. The large increases in the far future years of the time horizon are due to the permanence 

associated with damages from sea level rise, along with the assumption that the sea level is 

projected to continue to rise long after the global average temperature begins to decrease. The 

changes to the loss function generally decrease the interagency working group SCC estimates 

slightly given that relative increases in damages in later periods are discounted more heavily, all 

else equal.

14B.3.2 FUND

FUND version 3.8 includes a number of changes over the previous version 3.5 (Narita et 

al. 2010) used in the 2010 interagency report. Documentation supporting FUND and the model’s 

source code for all versions of the model is available from the model authors.h Notable changes, 

due to their impact on the SCC estimates, are adjustments to the space heating, agriculture, and 

sea level rise damage functions in addition to changes to the temperature response function and 

the inclusion of indirect effects from methane emissions.i

We discuss each of these in turn.

Space Heating

In FUND, the damages associated with the change in energy needs for space heating are 

based on the estimated impact due to one degree of warming. These baseline damages are scaled 

g The model and documentation, including formulas, are available on the author’s

webpage at http://www.econ.yale.edu/~nordhaus/homepage/RICEmodels.htm. h http://www.fund-model.org/. This report uses version 3.8 of the FUND model, which represents a modest update to 

the most recent version of the model to appear in the literature (version 3.7) (Anthoff and Tol, 2013). For the 

purpose of computing the SCC, the relevant changes (between 3.7 to 3.8) are associated with improving consistency 

with IPCC AR4 by adjusting the atmospheric lifetimes of CH4 and N2O and incorporating the indirect forcing 

effects of CH4, along with making minor stability improvements in the sea wall construction algorithm. i

The other damage sectors (water resources, space cooling, land loss, migration, ecosystems, human health, and 

extreme weather) were not significantly updated.

14B-6

 
based on the forecasted temperature anomaly’s deviation from the one degree benchmark and 

adjusted for changes in vulnerability due to economic and energy efficiency growth. In FUND 

3.5, the function that scales the base year damages adjusted for vulnerability allows for the 

possibility that in some simulations the benefits associated with reduced heating needs may be an 

unbounded convex function of the temperature anomaly. In FUND 3.8, the form of the scaling 

has been modified to ensure that the function is everywhere concave and that there will exist an 

upper bound on the benefits a region may receive from reduced space heating needs. The new 

formulation approaches a value of two in the limit of large temperature anomalies, or in other 

words, assuming no decrease in vulnerability, the reduced expenditures on space heating at any 

level of warming will not exceed two times the reductions experienced at one degree of 

warming. Since the reduced need for space heating represents a benefit of climate change in the 

model, or a negative damage, this change will increase the estimated SCC. This update accounts 

for a significant portion of the difference in the expected SCC estimates reported by the two 

versions of the model when run probabilistically.

Sea Level Rise and Land Loss

The FUND model explicitly includes damages associated with the inundation of dry land 

due to sea level rise. The amount of land lost within a region is dependent upon the proportion of 

the coastline being protected by adequate sea walls and the amount of sea level rise. In FUND 

3.5 the function defining the potential land lost in a given year due to sea level rise is linear in 

the rate of sea level rise for that year. This assumption implicitly assumes that all regions are 

well represented by a homogeneous coastline in length and a constant uniform slope moving 

inland. In FUND 3.8 the function defining the potential land lost has been changed to be a 

convex function of sea level rise, thereby assuming that the slope of the shore line increases 

moving inland. The effect of this change is to typically reduce the vulnerability of some regions 

to sea level rise based land loss, thereby lowering the expected SCC estimate. j

Agriculture

In FUND, the damages associated with the agricultural sector are measured as 

proportional to the sector’s value. The fraction is bounded from above by one and is made up of 

three additive components that represent the effects from carbon fertilization, the rate of 

temperature change, and the level of the temperature anomaly. In both FUND 3.5 and FUND 3.8, 

the fraction of the sector’s value lost due to the level of the temperature anomaly is modeled as a 

quadratic function with an intercept of zero. In FUND 3.5, the coefficients of this loss function 

are modeled as the ratio of two random normal variables. This specification had the potential for 

unintended extreme behavior as draws from the parameter in the denominator approached zero 

or went negative. In FUND 3.8, the coefficients are drawn directly from truncated normal 

distributions so that they remain in the range [0,∞) and (−∞,0], respectively, ensuring the correct 

sign and eliminating the potential for divide by zero errors. The means for the new distributions 

are set equal to the ratio of the means from the normal distributions used in the previous version. 

In general the impact of this change has been to decrease the range of the distribution while 

j

For stability purposes this report also uses an update to the model which assumes that regional coastal protection 

measures will be built to protect the most valuable land first, such that the marginal benefits of coastal protection is 

decreasing in the level of protection following Fankhauser (1995).

14B-7

 
spreading out the distributions’ mass over the remaining range relative to the previous version. 

The net effect of this change on the SCC estimates is difficult to predict. 

Transient Temperature Response 

The temperature response model translates changes in global levels of radiative forcing 

into the current expected temperature anomaly. In FUND, a given year’s increase in the 

temperature anomaly is based on a mean reverting function where the mean equals the 

equilibrium temperature anomaly that would eventually be reached if that year’s level of 

radiative forcing were sustained. The rate of mean reversion defines the rate at which the 

transient temperature approaches the equilibrium. In FUND 3.5, the rate of temperature response 

is defined as a decreasing linear function of equilibrium climate sensitivity to capture the fact 

that the progressive heat uptake of the deep ocean causes the rate to slow at higher values of the 

equilibrium climate sensitivity. In FUND 3.8, the rate of temperature response has been updated 

to a quadratic function of the equilibrium climate sensitivity. This change reduces the sensitivity 

of the rate of temperature response to the level of the equilibrium climate sensitivity, a 

relationship first noted by Hansen et al. (1985) based on the heat uptake of the deep ocean. 

Therefore in FUND 3.8, the temperature response will typically be faster than in the previous 

version. The overall effect of this change is likely to increase estimates of the SCC as higher 

temperatures are reached during the timeframe analyzed and as the same damages experienced in 

the previous version of the model are now experienced earlier and therefore discounted less.

Methane

The IPCC AR4 notes a series of indirect effects of methane emissions, and has developed 

methods for proxying such effects when computing the global warming potential of methane 

(Forster et al. 2007). FUND 3.8 now includes the same methods for incorporating the indirect 

effects of methane emissions. Specifically, the average atmospheric lifetime of methane has been 

set to 12 years to account for the feedback of methane emissions on its own lifetime. The 

radiative forcing associated with atmospheric methane has also been increased by 40% to 

account for its net impact on ozone production and stratospheric water vapor. All else equal, the 

effect of this increased radiative forcing will be to increase the estimated SCC values, due to 

greater projected temperature anomaly.

14B.3.3 PAGE

PAGE09 (Hope 2013) includes a number of changes from PAGE2002, the version used 

in the 2010 SCC interagency report. The changes that most directly affect the SCC estimates 

include: explicitly modeling the impacts from sea level rise, revisions to the damage function to 

ensure damages are constrained by GDP, a change in the regional scaling of damages, a revised 

treatment for the probability of a discontinuity within the damage function, and revised 

assumptions on adaptation. The model also includes revisions to the carbon cycle feedback and 

14B-8
the calculation of regional temperatures.k More details on PAGE09 can be found in Hope 

(2011a, 2011b, 2011c). A description of PAGE2002 can be found in Hope (2006).

Sea Level Rise

While PAGE2002 aggregates all damages into two categories – economic and noneconomic impacts –, PAGE09 adds a third explicit category: damages from sea level rise. In the 

previous version of the model, damages from sea level rise were subsumed by the other damage 

categories. In PAGE09 sea level damages increase less than linearly with sea level under the 

assumption that land, people, and GDP are more concentrated in low-lying shoreline areas. 

Damages from the economic and non-economic sector were adjusted to account for the 

introduction of this new category. 

Revised Damage Function to Account for Saturation 

In PAGE09, small initial economic and non-economic benefits (negative damages) are 

modeled for small temperature increases, but all regions eventually experience economic 

damages from climate change, where damages are the sum of additively separable polynomial 

functions of temperature and sea level rise. Damages transition from this polynomial function to 

a logistic path once they exceed a certain proportion of remaining Gross Domestic Product 

(GDP) to ensure that damages do not exceed 100 percent of GDP. This differs from PAGE2002, 

which allowed Eastern Europe to potentially experience large benefits from temperature 

increases, and which also did not bound the possible damages that could be experienced.

Regional Scaling Factors

As in the previous version of PAGE, the PAGE09 model calculates the damages for the 

European Union (EU) and then, assumes that damages for other regions are proportional based 

on a given scaling factor. The scaling factor in PAGE09 is based on the length of a region’s 

coastline relative to the EU (Hope 2011b). Because of the long coastline in the EU, other regions 

are, on average, less vulnerable than the EU for the same sea level and temperature increase, but 

all regions have a positive scaling factor. PAGE2002 based its scaling factors on four studies 

reported in the IPCC’s third assessment report, and allowed for benefits from temperature 

increase in Eastern Europe, smaller impacts in developed countries, and higher damages in 

developing countries. 

Probability of a Discontinuity

In PAGE2002, the damages associated with a “discontinuity” (nonlinear extreme event) 

were modeled as an expected value. Specifically, a stochastic probability of a discontinuity was 

multiplied by the damages associated with a discontinuity to obtain an expected value, and this 

was added to the economic and non-economic impacts. That is, additional damages from an 

extreme event, such as extreme melting of the Greenland ice sheet, were multiplied by the 

k Because several changes in the PAGE model are structural (e.g., the addition of sea level rise and treatment of 

discontinuity), it is not possible to assess the direct impact of each change on the SCC in isolation as done for the 

other two models above.

14B-9

 
probability of the event occurring and added to the damage estimate. In PAGE09, the probability 

of discontinuity is treated as a discrete event for each year in the model. The damages for each 

model run are estimated either with or without a discontinuity occurring, rather than as an 

expected value. A large‐scale discontinuity becomes possible when the temperature rises beyond 

some threshold value between 2 and 4°C. The probability that a discontinuity will occur beyond 

this threshold then increases by between 10 and 30 percent for every 1°C rise in temperature 

beyond the threshold. If a discontinuity occurs, the EU loses an additional 5 to 25 percent of its 

GDP (drawn from a triangular distribution with a mean of 15 percent) in addition to other 

damages, and other regions lose an amount determined by the regional scaling factor. The 

threshold value for a possible discontinuity is lower than in PAGE2002, while the rate at which 

the probability of a discontinuity increases with the temperature anomaly and the damages that 

result from a discontinuity are both higher than in PAGE2002. The model assumes that only one 

discontinuity can occur and that the impact is phased in over a period of time, but once it occurs, 

its effect is permanent.

Adaptation

As in PAGE2002, adaptation is available to help mitigate any climate change impacts 

that occur. In PAGE this adaptation is the same regardless of the temperature change or sea level 

rise and is therefore akin to what is more commonly considered a reduction in vulnerability. It is 

modeled by reducing the damages by some percentage. PAGE09 assumes a smaller decrease in 

vulnerability than the previous version of the model and assumes that it will take longer for this 

change in vulnerability to be realized. In the aggregated economic sector, at the time of full 

implementation, this adaptation will mitigate all damages up to a temperature increase of 1°C, 

and for temperature anomalies between 1°C and 2°C, it will reduce damages by 15-30 percent 

(depending on the region). However, it takes 20 years to fully implement this adaptation. In 

PAGE2002, adaptation was assumed to reduce economic sector damages up to 2°C by 50-90 

percent after 20 years. Beyond 2°C, no adaptation is assumed to be available to mitigate the

impacts of climate change. For the non-economic sector, in PAGE09 adaptation is available to 

reduce 15 percent of the damages due to a temperature increase between 0°C and 2°C and is 

assumed to take 40 years to fully implement, instead of 25 percent of the damages over 20 years 

assumed in PAGE2002. Similarly, adaptation is assumed to alleviate 25-50 percent of the 

damages from the first 0.20 to 0.25 meters of sea level rise but is assumed to be ineffective 

thereafter. Hope (2011c) estimates that the less optimistic assumptions regarding the ability to 

offset impacts of temperature and sea level rise via adaptation increase the SCC by 

approximately 30 percent.

Other Noteworthy Changes

Two other changes in the model are worth noting. There is a change in the way the model 

accounts for decreased CO2 absorption on land and in the ocean as temperature rises. PAGE09 

introduces a linear feedback from global mean temperature to the percentage gain in the excess 

concentration of CO2, capped at a maximum level. In PAGE2002, an additional amount was 

added to the CO2 emissions each period to account for a decrease in ocean absorption and a loss 

of soil carbon. Also updated is the method by which the average global and annual temperature 

anomaly is downscaled to determine annual average regional temperature anomalies to be used 

in the regional damage functions. In PAGE2002, the scaling was determined solely based on 

14B-10
regional difference in emissions of sulfate aerosols. In PAGE09, this regional temperature 

anomaly is further adjusted using an additive factor that is based on the average absolute latitude 

of a region relative to the area weighted average absolute latitude of the Earth’s landmass, to 

capture relatively greater changes in temperature forecast to be experienced at higher latitudes.

14B.4 REVISED SCC ESTIMATES

The updated versions of the three integrated assessment models were run using the same 

methodology detailed in the 2010 TSD (Interagency Working Group on Social Cost of Carbon 

2010). The approach along with the inputs for the socioeconomic emissions scenarios, 

equilibrium climate sensitivity distribution, and discount rate remains the same. This includes the 

five reference scenarios based on the EMF-22 modeling exercise, the Roe and Baker equilibrium 

climate sensitivity distribution calibrated to the IPCC AR4, and three constant discount rates of 

2.5, 3, and 5 percent.

As was previously the case, the use of three models, three discount rates, and five 

scenarios produces 45 separate distributions for the global SCC. The approach laid out in the 

2010 TSD applied equal weight to each model and socioeconomic scenario in order to reduce the 

dimensionality down to three separate distributions representative of the three discount rates. The 

interagency group selected four values from these distributions for use in regulatory analysis. 

Three values are based on the average SCC across models and socio-economic-emissions 

scenarios at the 2.5-, 3-, and 5-percent discount rates, respectively. The fourth value was chosen 

to represent the higher-than-expected economic impacts from climate change further out in the 

tails of the SCC distribution. For this purpose, the 95th percentile of the SCC estimates at a 3-

percent discount rate was chosen. (A detailed set of percentiles by model and scenario 

combination and additional summary statistics for the 2020 values is available in the Appendix.)

As noted in the 2010 TSD, “the 3 percent discount rate is the central value, and so the central 

value that emerges is the average SCC across models at the 3 percent discount rate” (Interagency 

Working Group on Social Cost of Carbon 2010, p. 25). However, for purposes of capturing the 

uncertainties involved in regulatory impact analysis, the interagency group emphasizes the 

importance and value of including all four SCC values.

Table 14B.4.2 shows the four selected SCC estimates in five year increments from 2010 

to 2050. Values for 2010, 2020, 2030, 2040, and 2050 are calculated by first combining all 

outputs (10,000 estimates per model run) from all scenarios and models for a given discount rate. 

Values for the years in between are calculated using linear interpolation. The full set of revised 

annual SCC estimates between 2010 and 2050 is reported in the Appendix.

14B-11
Table 14B.4.1 Revised Social Cost of CO2, 2010–2050 (in 2007 dollars per metric ton of 

CO2)

Discount Rate 5.0% 3.0% 2.5% 3.0%

Year Avg Avg Avg 95th

2010 11 32 51 89

2015 12 37 57 109

2020 12 43 64 128

2025 14 47 69 143

2030 16 52 75 159

2035 19 56 80 175

2040 21 61 86 191

2045 24 66 92 206

2050 26 71 97 220

The SCC estimates using the updated versions of the models are higher than those 

reported in the 2010 TSD due to the changes to the models outlined in the previous section. By 

way of comparison, the 2020 SCC estimates reported in the original TSD were $7, $26, $42 and 

$81 (2007$) (Interagency Working Group on Social Cost of Carbon 2010). Figure 14B.4.1

illustrates where the four SCC values for 2020 fall within the full distribution for each discount 

rate based on the combined set of runs for each model and scenario (150,000 estimates in total 

for each discount rate). In general, the distributions are skewed to the right and have long tails. 

The Figure also shows that the lower the discount rate, the longer the right tail of the distribution.

Figure 14B.4.1 Distribution of SCC Estimates for 2020 (in 2007$ per metric ton CO2)

As was the case in the 2010 TSD, the SCC increases over time because future emissions 

are expected to produce larger incremental damages as physical and economic systems become 

more stressed in response to greater climatic change. The approach taken by the interagency 

group is to compute the cost of a marginal ton emitted in the future by running the models for a 

14B-12
set of perturbation years out to 2050. Table 14B.4.2 illustrates how the growth rate for these four 

SCC estimates varies over time. 

Table 14B.4.2 Average Annual Growth Rates of SCC Estimates between 2010 and 2050

Average Annual Growth 5.0% 3.0% 2.5% 3.0%

Rate (%) Avg Avg Avg 95th

2010-2020 1.2% 3.3% 2.4% 4.4%

2020-2030 3.4% 2.1% 1.7% 2.4%

2030-2040 3.0% 1.9% 1.5% 2.1%

2040-2050 2.6% 1.6% 1.3% 1.5%

The future monetized value of emission reductions in each year (the SCC in year t

multiplied by the change in emissions in year t) must be discounted to the present to determine 

its total net present value for use in regulatory analysis. As previously discussed in the 2010 

TSD, damages from future emissions should be discounted at the same rate as that used to 

calculate the SCC estimates themselves to ensure internal consistency – i.e., future damages from 

climate change, whether they result from emissions today or emissions in a later year, should be 

discounted using the same rate. 

Under current OMB guidance contained in Circular A-4, analysis of economically 

significant proposed and final regulations from the domestic perspective is required, while 

analysis from the international perspective is optional. However, the climate change problem is 

highly unusual in at least two respects. First, it involves a global externality: emissions of most 

greenhouse gases contribute to damages around the world even when they are emitted in the 

United States. Consequently, to address the global nature of the problem, the SCC must 

incorporate the full (global) damages caused by GHG emissions. Second, climate change 

presents a problem that the United States alone cannot solve. Even if the United States were to 

reduce its greenhouse gas emissions to zero, that step would be far from enough to avoid 

substantial climate change. Other countries would also need to take action to reduce emissions if 

significant changes in the global climate are to be avoided. Emphasizing the need for a global 

solution to a global problem, the United States has been actively involved in seeking 

international agreements to reduce emissions and in encouraging other nations, including 

emerging major economies, to take significant steps to reduce emissions. When these 

considerations are taken as a whole, the interagency group concluded that a global measure of 

the benefits from reducing U.S. emissions is preferable. For additional discussion, see the 2010 

TSD.

14B.5 OTHER MODEL LIMITATIONS AND RESEARCH GAPS

The 2010 interagency SCC TSD discusses a number of important limitations for which 

additional research is needed. In particular, the document highlights the need to improve the 

quantification of both non-catastrophic and catastrophic damages, the treatment of adaptation 

and technological change, and the way in which inter-regional and inter-sectoral linkages are 

modeled. While the new version of the models discussed above offer some improvements in 

these areas, further work remains warranted. The 2010 TSD also discusses the need to more 

carefully assess the implications of risk aversion for SCC estimation as well as the inability to 

14B-13
perfectly substitute between climate and non-climate goods at higher temperature increases, both 

of which have implications for the discount rate used. EPA, DOE, and other agencies continue to 

engage in research on modeling and valuation of climate impacts that can potentially improve

SCC estimation in the future. 

14B-14
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Anthoff, D. and Tol, R.S.J. 2013a. The uncertainty about the social cost of carbon: a 

decomposition analysis using FUND. Climatic Change 117: 515–530.

Anthoff, D. and Tol, R.S.J. 2013b. Erratum to: The uncertainty about the social cost of carbon: A 

decomposition analysis using FUND. Climatic Change. Advance online publication. doi: 

10.1007/s10584-013-0959-1.

Fankhauser, S. 1995. Valuing climate change: The economics of the greenhouse. London, 

England: Earthscan.

Forster, P., V. Ramaswamy, P. Artaxo, T. Berntsen, R. Betts, D.W. Fahey, J. Haywood, J. Lean, 

D.C. Lowe, G. Myhre, J. Nganga, R. Prinn, G. Raga, M. Schulz and R. Van Dorland. 2007. 

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Marquis, K.B. Averyt, M.Tignor and H.L. Miller (eds.)]. Cambridge University Press, 

Cambridge, United Kingdom and New York, NY, USA.

Hope, Chris. 2006. “The Marginal Impact of CO2 from PAGE2002: An Integrated Assessment 

Model Incorporating the IPCC’s Five Reasons for Concern.” The Integrated Assessment Journal. 

6(1): 19–56.

Hope, Chris. 2011a “The PAGE09 Integrated Assessment Model: A Technical Description” 

Cambridge Judge Business School Working Paper No. 4/2011 (April). Accessed November 23, 

2011: http://www.jbs.cam.ac.uk/research/working_papers/2011/wp1104.pdf.

Hope, Chris. 2011b “The Social Cost of CO2 from the PAGE09 Model” Cambridge Judge 

Business School Working Paper No. 5/2011 (June). Accessed November 23, 2011: 

http://www.jbs.cam.ac.uk/research/working_papers/2011/wp1105.pdf.

Hope, Chris. 2011c “New Insights from the PAGE09 Model: The Social Cost of CO2” 

Cambridge Judge Business School Working Paper No. 8/2011 (July). Accessed November 23, 

2011: http://www.jbs.cam.ac.uk/research/working_papers/2011/wp1108.pdf.

Hope, C. 2013. Critical issues for the calculation of the social cost of CO2: why the estimates 

from PAGE09 are higher than those from PAGE2002. Climatic Change 117: 531–543.

Interagency Working Group on Social Cost of Carbon. 2010. Social Cost of Carbon for 

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Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. 

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14B-16
APPENDIX A

Table A1 Annual SCC Values: 2010-2050 (2007$/metric ton CO2)

Discount Rate 5.0% 3.0% 2.5% 3.0%

Year Avg Avg Avg 95th

2010 11 32 51 89

2011 11 33 52 93

2012 11 34 54 97

2013 11 35 55 101

2014 11 36 56 105

2015 11 37 57 109

2016 12 38 59 112

2017 12 39 60 116

2018 12 40 61 120

2019 12 42 62 124

2020 12 43 64 128

2021 12 43 65 131

2022 13 44 66 134

2023 13 45 67 137

2024 14 46 68 140

2025 14 47 69 143

2026 15 48 70 146

2027 15 49 71 149

2028 15 50 72 152

2029 16 51 73 155

2030 16 52 75 159

2031 17 52 76 162

2032 17 53 77 165

2033 18 54 78 168

2034 18 55 79 172

2035 19 56 80 175

2036 19 57 81 178

2037 20 58 83 181

2038 20 59 84 185

2039 21 60 85 188

2040 21 61 86 191

2041 22 62 87 194

2042 22 63 88 197

2043 23 64 89 200

2044 23 65 90 203

2045 24 66 92 206

2046 24 67 93 209

2047 25 68 94 211

2048 25 69 95 214

2049 26 70 96 217

2050 26 71 97 220

14B-17
Table A2 2020 Global SCC Estimates at 2.5-Percent Discount Rate (2007$/metric ton CO2)

Percentile 1st 5th 10th 25th 50th Avg 75th 90th 95th 99th

Scenariol PAGE

IMAGE 6 11 15 27 58 129 139 327 515 991

MERGE Optimistic 4 6 9 16 34 78 82 196 317 649

MESSAGE 4 8 11 20 42 108 107 278 483 918

MiniCAM Base 5 9 12 22 47 107 113 266 431 872

5th Scenario 2 4 6 11 25 85 68 200 387 955

Scenario DICE

IMAGE 25 31 37 47 64 72 92 123 139 161

MERGE Optimistic 14 18 20 26 36 40 50 65 74 85

MESSAGE 20 24 28 37 51 58 71 95 109 221

MiniCAM Base 20 25 29 38 53 61 76 102 117 135

5th Scenario 17 22 25 33 45 52 65 91 106 126

Scenario FUND

IMAGE -14 -2 4 15 31 39 55 86 107 157

MERGE Optimistic -6 1 6 14 27 35 46 70 87 141

MESSAGE -16 -5 1 11 24 31 43 67 83 126

MiniCAM Base -7 2 7 16 32 39 55 83 103 158

5th Scenario -29 -13 -6 4 16 21 32 53 69 103

Table A3 2020 Global SCC Estimates at 3-Percent Discount Rate (2007$/metric ton CO2)

Percentile 1st 5th 10th 25th 50th Avg 75th 90th 95th 99th

Scenario PAGE

IMAGE 4 7 10 18 38 91 95 238 385 727

MERGE Optimistic 2 4 6 11 23 56 58 142 232 481

MESSAGE 3 5 7 13 29 75 74 197 330 641

MiniCAM Base 3 5 8 14 30 73 75 184 300 623

5th Scenario 1 3 4 7 17 58 48 136 264 660

Scenario DICE

IMAGE 16 21 24 32 43 48 60 79 90 102

MERGE Optimistic 10 13 15 19 25 28 35 44 50 58

MESSAGE 14 18 20 26 35 40 49 64 73 83

MiniCAM Base 13 17 20 26 35 39 49 65 73 85

5th Scenario 12 15 17 22 30 34 43 58 67 79

Scenario FUND

IMAGE -13 -4 0 8 18 23 33 51 65 99

MERGE Optimistic -7 -1 2 8 17 21 29 45 57 95

MESSAGE -14 -6 -2 5 14 18 26 41 52 82

MiniCAM Base -7 -1 3 9 19 23 33 50 63 101

5th Scenario -22 -11 -6 1 8 11 18 31 40 62

l

See 2010 TSD for a description of these scenarios.

14B-18

 
Table A4 2020 Global SCC Estimates at 5-Percent Discount Rate (2007$/metric ton CO2)

Percentile 1st 5th 10th 25th 50th Avg 75th 90th 95th 99th

Scenario PAGE

IMAGE 1 2 2 5 10 28 27 71 123 244

MERGE Optimistic 1 1 2 3 7 17 17 45 75 153

MESSAGE 1 1 2 4 9 24 22 60 106 216

MiniCAM Base 1 1 2 3 8 21 21 54 94 190

5th Scenario 0 1 1 2 5 18 14 41 78 208

Scenario DICE

IMAGE 6 8 9 11 14 15 18 22 25 27

MERGE Optimistic 4 5 6 7 9 10 12 15 16 18

MESSAGE 6 7 8 10 12 13 16 20 22 25

MiniCAM Base 5 6 7 8 11 12 14 18 20 22

5th Scenario 5 6 6 8 10 11 14 17 19 21

Scenario FUND

IMAGE -9 -5 -4 -1 2 3 6 10 14 24

MERGE Optimistic -6 -4 -2 0 3 4 6 11 15 26

MESSAGE -10 -6 -4 -1 1 2 5 9 12 21

MiniCAM Base -7 -4 -2 0 3 4 6 11 14 25

5th Scenario -11 -7 -5 -3 0 0 3 5 7 13

14B-19
Table A5 Additional Summary Statistics of 2020 Global SCC Estimates

Discount rate: 5.0% 3.0% 2.5%

Statistic: Mean Variance Skewness Kurtosis Mean Variance Skewness Kurtosis Mean Variance Skewness Kurtosis

DICE 12 26 2 15 38 409 3 24 57 1097 3 30

PAGE 22 1616 5 32 71 14953 4 22 101 29312 4 23

FUND 3 41 5 179 19 1452 -42 8727 33 6154 -73 14931

14B-20
APPENDIX B

The November 2013 revision of this technical support document is based on two 

corrections to the runs based on the FUND model. First, the potential dry land loss in the 

algorithm that estimates regional coastal protections was misspecified in the model’s computer 

code. This correction is covered in an erratum to Anthoff and Tol (2013) published in the same 

journal (Climatic Change) in October 2013 (Anthoff and Tol (2013b)). Second, the equilibrium 

climate sensitivity distribution was inadvertently specified as a truncated Gamma distribution 

(the default in FUND) as opposed to the truncated Roe and Baker distribution as was intended. 

The truncated Gamma distribution used in the FUND runs had approximately the same mean and 

upper truncation point, but lower variance and faster decay of the upper tail, as compared to the 

intended specification based on the Roe and Baker distribution. The difference between the 

original estimates reported in the May 2013 version of this technical support document and this 

revision are all one dollar or less.

14B-21
CHAPTER 15. UTILITY IMPACT ANALYSIS

TABLE OF CONTENTS

15.1 INTRODUCTION ......................................................................................................... 15-1

15.2 METHODOLOGY ........................................................................................................ 15-1

15.3 UTILITY IMPACT RESULTS ..................................................................................... 15-2

15.3.1 Installed Capacity............................................................................................... 15-2

15.3.2 Electricity Generation ........................................................................................ 15-6

15.3.3 Results Summary ............................................................................................... 15-9

REFERENCES ........................................................................................................................ 15-10

LIST OF FIGURES

Figure 15.3.1 Automatic Commercial Ice Makers: Total Capacity Reduction ......................... 15-3

Figure 15.3.2 Automatic Commercial Ice Makers: Coal Capacity Reduction .......................... 15-3

Figure 15.3.3 Automatic Commercial Ice Makers: Nuclear Capacity Reduction ..................... 15-4

Figure 15.3.4 Automatic Commercial Ice Makers: Gas Combined Cycle Capacity Reduction 15-4

Figure 15.3.5 Automatic Commercial Ice Makers: Peaking Capacity Reduction..................... 15-5

Figure 15.3.6 Automatic Commercial Ice Makers: Renewables Capacity Reduction .............. 15-5

Figure 15.3.7 Automatic Commercial Ice Makers: Total Generation Reduction...................... 15-6

Figure 15.3.8 Automatic Commercial Ice Makers: Coal Generation Reduction....................... 15-6

Figure 15.3.9 Automatic Commercial Ice Makers: Nuclear Generation Reduction.................. 15-7

Figure 15.3.10 Automatic Commercial Ice Makers: Gas Combined Cycle Generation Reduction

............................................................................................................................ 15-7

Figure 15.3.11 Automatic Commercial Ice Makers: Oil Generation Reduction ....................... 15-8

Figure 15.3.12 Automatic Commercial Ice Makers: Renewables Generation Reduction......... 15-8

LIST OF TABLES

Table 15.3.1 Automatic Commercial Ice Makers: Summary of Utility Impact Results............ 15-9

15-i
CHAPTER 15. UTILITY IMPACT ANALYSIS

15.1 INTRODUCTION

In the utility impact analysis, U.S. Department of Energy (DOE) analyzes the changes in 

electric installed capacity and generation that result for each trial standard level (TSL).

The utility impact analysis uses a variant of the DOE Energy Information 

Administration’s (EIA’s) National Energy Modeling System (NEMS).a NEMS is a public 

domain, multi-sectored, partial equilibrium model of the U.S. energy sector. Each year, 

DOE/EIA uses NEMS to produce an energy forecast for the United States, the Annual Energy 

Outlook (AEO). DOE uses a variant of this model, referred to as NEMS-BT,b to account for 

selected utility impacts of energy conservation standards. DOE’s analysis consists of a 

comparison between model results for the most recent AEO Reference Case and for cases in 

which energy use is decremented to reflect the impact of standards. For the analysis of standards 

on automatic commercial ice makers, DOE used the version of NEMS based on AEO2014.

1

NEMS-BT has a number of advantages that have led to its use in the analysis of energy 

conservation standards:

• NEMS-BT uses a set of assumptions that are well known and fairly transparent, due 

to the exposure and scrutiny each AEO receives.

• NEMS-BT is updated each year, with each edition of the AEO, to reflect changes in 

energy prices, supply trends, regulations, etc. 

• The comprehensiveness of NEMS-BT permits the modeling of interactions among the 

various energy supply and demand sectors. 

15.2 METHODOLOGY 

DOE uses NEMS-BT to estimate the marginal impacts of reduction in energy demand on 

the energy supply sector. In principle, marginal values should provide a better estimate of the 

actual impact of energy conservation standards. In practice, the numerical differences between 

marginal and average values may turn out to be smaller than the intrinsic uncertainties in the 

AEO.

NEMS uses predicted growth in demand for each end use to build up a projection of the 

total electric system load growth. The system load shapes are converted internally to load 

duration curves, which are then used to estimate the most cost-effective additions to capacity. In 

general, when electricity demand deviates from the AEO Reference Case, three inter-related 

a For more information on NEMS, refer to the U.S. Department of Energy, Energy Information Administration 

documentation. A useful summary is National Energy Modeling System: An Overview 2003, DOE/EIA-0581(2003), 

March 2003.

b DOE/EIA approves use of the name NEMS to describe only an official version of the model without any 

modification to code or data. Because this analysis entails some minor code modifications and the model is run 

under various policy scenarios that are variations on DOE/EIA assumptions, DOE refers to it by the name NEMSBT. (BT is DOE’s Building Technologies Program, under whose aegis this work has been performed.) 

15-1

 
effects can occur: the annual generation (terawatt-hours (TWh)) from the stock of electric 

generating capacity changes; the total generation capacity itself (gigawatts (GW)) may change, 

and the mix of capacity by fuel type may change. Each of these effects can vary for different 

types of end use. The change in total generating capacity is sensitive to the degree to which the 

end-use is peak coincident, while the capacity mix is sensitive to the hourly load shape 

associated with the end use.

To model the impact of a standard, DOE inputs a reduction to annual energy demand for 

the corresponding end use in the appropriate start year. The NEMS-BT model is run with the 

decremented energy demand to determine the modified build-out of capacity and total 

generation. Regional effects of a standard can be accounted for by defining the energy demand 

decrement as a function of census division.

The output of the NEMS-BT analysis includes the effective marginal heat rate (i.e., ratio 

of the change in fuel consumption in quadrillion British thermal units (quads) to the change in 

generation in TWh) and the capacity reduction by fuel type for a given reduction in total 

generation. DOE uses the site-energy savings multiplied by a transmission and distribution loss 

factor to estimate the reduction in generation for each TSL. The relationship between a 

reductionc in electricity generation (TWh) and the reduction in capacity (GW) is estimated based 

on the output of NEMS-BT model runs using the end-use-specific energy demand decrement. 

Details on this approach used may be found in Coughlin (2013).2

NEMS-BT provides output for the following capacity types: coal, nuclear, combined

cycle (natural gas), renewable sources, oil and natural gas steam, combustion turbine/diesel, 

pumped storage, fuel cells, and distributed generation (natural gas). DOE grouped oil and natural

gas steam and combustion turbine/diesel into a peaking category, and grouped pumped storage, 

fuel cells, and distributed generation (natural gas) into an “other” category.

In general, energy conservation standards impact primarily fossil combustion (i.e., coal,

natural gas and diesel) and renewables. Pumped storage and nuclear power are very insensitive 

to small changes in demand, while fuel cells and distributed generation make up a very small 

fraction (less than 1 percent) of the generation capacity base.

15.3 UTILITY IMPACT RESULTS

This section presents results of the analysis for all of the capacity types except “other,” 

for which the impacts are very small.

15.3.1 Installed Capacity

Figure 15.3.1 through Figure 15.3.6 show the changes in U.S. electricity installed 

capacity that result for each TSL by major plant type for selected years.

c These reductions are defined relative to the AEO Reference Case.

15-2

 
Figure 15.3.1 Automatic Commercial Ice Makers: Total Capacity Reduction

Figure 15.3.2 Automatic Commercial Ice Makers: Coal Capacity Reduction

0

50

100

150

200

250

300

350

400

450

201820232028203320382043204820532058

MW

Total

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

0

10

20

30

40

50

60

70

80

90

100

201820232028203320382043204820532058

MW

Coal

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

15-3
Figure 15.3.3 Automatic Commercial Ice Makers: Nuclear Capacity Reduction

Figure 15.3.4 Automatic Commercial Ice Makers: Gas Combined Cycle Capacity 

Reduction

0

1

2

3

4

5

6

7

8

201820232028203320382043204820532058

MW

Nuclear

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

0

20

40

60

80

100

120

140

160

201820232028203320382043204820532058

MW

NGCC

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

15-4
Figure 15.3.5 Automatic Commercial Ice Makers: Peaking Capacity Reduction

Figure 15.3.6 Automatic Commercial Ice Makers: Renewables Capacity Reduction

0

20

40

60

80

100

120

201820232028203320382043204820532058

MW

Peaking

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

0

10

20

30

40

50

60

70

201820232028203320382043204820532058

MW

Renewable

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

15-5
15.3.2 Electricity Generation

Figure 15.3.7 through Figure 15.3.12 show the annual changes in electricity generation 

that result for each TSL by plant type. Gas combined cycle power plants account for most of the 

generation reduction. 

Figure 15.3.7 Automatic Commercial Ice Makers: Total Generation Reduction

Figure 15.3.8 Automatic Commercial Ice Makers: Coal Generation Reduction

0

200

400

600

800

1000

1200

1400

1600

201820232028203320382043204820532058

GWh

Total

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

0

100

200

300

400

500

600

201820232028203320382043204820532058

GWh

Coal

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

15-6
Figure 15.3.9 Automatic Commercial Ice Makers: Nuclear Generation Reduction

Figure 15.3.10 Automatic Commercial Ice Makers: Gas Combined Cycle Generation 

Reduction

0

10

20

30

40

50

60

201820232028203320382043204820532058

GWh

Nuclear

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

0

100

200

300

400

500

600

700

800

201820232028203320382043204820532058

GWh

NGCC

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

15-7
Figure 15.3.11 Automatic Commercial Ice Makers: Oil Generation Reduction

Figure 15.3.12 Automatic Commercial Ice Makers: Renewables Generation Reduction

0

1

1

2

2

3

3

201820232028203320382043204820532058

GWh

Oil

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

0

50

100

150

200

250

201820232028203320382043204820532058

GWh

Renewable

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

15-8
15.3.3 Results Summary

Table 15.3.1 presents a summary of the utility impact results for automatic commercial 

ice makers.

Table 15.3.1 Automatic Commercial Ice Makers: Summary of Utility Impact Results

TSL

1 2 3 4 5

Installed Capacity Reduction 

MW

2020 22.82 38.36 50.63 64.64 90.83

2025 66.87 112.40 148.36 189.40 266.16

2030 74.31 124.91 164.87 210.48 295.78

2035 83.09 139.67 184.36 235.35 330.73

2040 92.59 155.64 205.43 262.26 368.54

Electricity Generation Reduction

GWh

2020 104.56 175.75 231.98 296.15 416.16 

2025 286.46 481.49 635.55 811.35 1,140.15 

2030 295.27 496.31 655.11 836.33 1,175.25 

2035 305.81 514.02 678.48 866.17 1,217.17 

2040 317.98 534.48 705.49 900.65 1,265.64 

15-9
REFERENCES

1. U.S. Department of Energy, Energy Information Administration, Annual Energy Outlook 

2014. 2014. Washington, DC. (Last accessed May 1, 2014.) 

<http://www.eia.gov/forecasts/aeo/>.

2. Coughlin, K. 2013. Projections of Full-Fuel-Cycle Energy and Emissions Metrics. 

Lawrence Berkeley National Laboratory, Report Number LBNL-6025E.

15-10
CHAPTER 16. EMPLOYMENT IMPACT ANALYSIS

TABLE OF CONTENTS

16.1 INTRODUCTION ............................................................................................................ 16-1

16.2 ASSUMPTIONS............................................................................................................... 16-1

16.3 METHODOLOGY ........................................................................................................... 16-1

16.4 SHORT-TERM RESULTS............................................................................................... 16-2

16.5 LONG-TERM RESULTS................................................................................................. 16-3

REFERENCES .......................................................................................................................... 16-5

LIST OF TABLES

Table 16.4.1 Net Short-Term Change in Employment (Number of Employees)...................... 16-3

16-i
CHAPTER 16. EMPLOYMENT IMPACT ANALYSIS

16.1 INTRODUCTION

The U.S. Department of Energy’s (DOE’s) employment impact analysis is designed to 

estimate indirect national job creation or elimination resulting from proposed standards due to 

reallocation of associated expenditures for purchasing and operating automatic commercial icemaking (ACIM) equipment. Job increases or decreases reported in this chapter are separate from 

the direct ACIM sector employment impacts reported in chapter 12 and reflect the employment 

impact of efficiency standards on all other sectors of the economy. DOE conducted this analysis 

as part of the final rule.

16.2 ASSUMPTIONS

DOE expects amended energy conservation standards to decrease energy use and,

therefore, reduce energy expenditures. Because at least one design option would also reduce 

water usage, the electricity usage standards could also have the effect of reducing water 

expenditures as well. The savings in energy and water expenditures may be spent on new 

investments or not spent at all (i.e., they may remain “saved”). Amended standards may increase 

the purchase price of automatic commercial ice makers, including the retail price plus sales tax, 

and could, in some cases, increase installation costs.

Using an input/output econometric model of the U.S. economy, this analysis estimated 

the short-term effect of these expenditure impacts on net economic output and employment. 

DOE intends this analysis to quantify the indirect employment impacts of these expenditure 

changes. DOE evaluated direct employment impacts at manufacturers’ facilities in the 

manufacturer impact analysis (see chapter 12).

DOE notes that the Impact of Sector Energy Technologies (ImSET) model is not a 

general equilibrium forecasting model and understands the uncertainties involved in projecting 

employment impacts, especially changes in the later years of the analysis.1 Because ImSET does 

not incorporate price changes, the employment effects predicted by ImSET would overestimate 

the magnitude of actual job impacts over the long run for this rule. Because input/output models 

do not allow prices to bring markets into equilibrium, they are best used for short-run analysis. 

Therefore, DOE included a qualitative discussion of how labor markets are likely to respond in 

the longer term. In future rulemakings, DOE may consider the use of other modeling approaches 

for examining long-run employment impacts.

16.3 METHODOLOGY

DOE based its analysis on an input/output model of the U.S. economy that estimates the 

effects of standards on major sectors of the economy related to buildings and the net impact of 

standards on jobs. Pacific Northwest National Laboratory (PNNL) developed the model using

ImSET 3.1.12

as a successor to Impact of Building Energy Efficiency Programs (ImBuild),

3 a 

special purpose version of the Impact Analysis for Planning (IMPLAN)

4 national input/output

model. ImSET estimates the employment and income effects of building energy technologies. 

16-1
Compared with simple economic multiplier approaches, ImSET allows for more complete and 

automated analysis of the economic impacts of energy-efficiency investments in buildings.

In an input/output model, the level of employment in an economy is determined by the 

relationships of different sectors of the economy and spending flows among them. Different 

sectors have different levels of labor intensity, and so changes in the level of spending (e.g., due 

to the effects of an efficiency standard) in one sector of the economy will affect flows in other 

sectors, which affects the overall level of employment.

ImSET uses a 187-sector model of the national economy to predict the economic effects 

of residential and commercial buildings’ technologies. ImSET collects estimates of initial 

investments, energy savings, and economic activity associated with spending the savings 

resulting from standards (e.g., changes in final demand in personal consumption, business 

investment and spending, and government spending). It provides overall estimates of the change 

in national output for each input/output sector. The model applies estimates of employment and

wage income per dollar of economic output for each sector and calculates impacts on national 

employment and wage income.

Energy-efficiency technology primarily affects the U.S. economy along three spending 

pathways. First, general investment funds are diverted to sectors that manufacture, install, and 

maintain energy-efficient equipment. The increased cost of equipment leads to higher 

employment in the equipment manufacturing sectors and lower employment in other economic 

sectors. Second, commercial firm and residential spending are redirected from utilities toward 

firms that supply production inputs. Third, utility sector investment funds are released for use in 

other sectors of the economy. When customers use less energy or water, electric and/or water 

utilities experience relative reductions in demand, which leads to reductions in utility sector 

investment and employment.

DOE notes that the employment impacts estimated with ImSET for the entire economy 

differ from the employment impacts in the ACIM manufacturing sector estimated in chapter 12 

using the Government Regulatory Impact Model (GRIM). The methodologies used and the 

sectors analyzed in the ImSET and GRIM models are different.

16.4 SHORT-TERM RESULTS

The results in this section refer to impacts of ACIM standards relative to the base case. 

DOE disaggregated the impact of standards on employment into three component effects: 

increased capital investment costs, decreased energy and water costs, and changes in operations 

and maintenance costs. 

Conceptually, one can consider the impact of the rule in its first year on three aggregate 

sectors: the ACIM production sector, the energy generation sector, and the general consumer 

goods sector (as mentioned previously, ImSET’s calculations are made at a much more 

disaggregate level). By raising energy efficiency, the rule increases the purchase price of 

automatic commercial ice makers; this increase in expenditures causes an increase in 

employment in this sector. At the same time, the improvements in energy efficiency reduce 

expenditures on electricity. The reduction in electricity demand causes a reduction in 

16-2
employment in that sector. Finally, based on the net impact of increased expenditures on 

automatic commercial ice makers and reduced expenditures on electricity, expenditures on 

everything else are either positively or negatively affected, increasing or reducing jobs in that 

sector accordingly. The model also captures any indirect jobs created or lost by changes in 

consumption due to changes in employment (e.g., as more workers are hired, they consume more 

goods, which generates more employment; the converse is true for workers laid off). 

Table 16.4.1 presents the modeled net employment impact from the rule in 2018 and 

2021. As mentioned in chapter 12, 84 percent of automatic commercial ice makers are produced 

domestically and 16 percent are imported. The net employment impact estimate is sensitive to 

assumptions regarding the return to the U.S. economy of money spent on imported automatic 

commercial ice makers. The two scenarios bounding the ranges presented in Table 16.4.1

represent situations in which none of the money spent on imported automatic commercial ice 

makers returns to the U.S. economy and all of the money spent on imported automatic 

commercial ice makers returns to the U.S. economy (low and high bounds, respectively). The 

U.S. trade deficit in recent years suggests that between 50 and 75 percent of the money spent on 

imported automatic commercial ice makers is likely to return, with employment impacts falling 

within the ranges presented below.

Table 16.4.1 Net Short-Term Change in Employment (Number of Employees)

Trial Standard Level 2018* 2022

1 18 to 21 104 to 107

2 31 to 38 196 to 204 

3 41 to 52 263 to 276

4 41 to 63 315 to 340

5 4 to 82 376 to 464

* Compliance date of standard levels is 2018.

16.5 LONG-TERM RESULTS

Due to the short payback period of energy-efficiency improvements mandated by this 

rule, over the long term DOE expects the energy savings to customers to increasingly dominate 

the increase in equipment costs, resulting in increased aggregate savings to customers. As a 

result, DOE expects demand for electricity to decrease over time and demand for other goods to 

increase. Because the electricity generation sector is relatively capital intensive compared to the 

consumer goods sector, the net effect will be an increase in labor demand. In equilibrium, this 

should lead to upward pressure on wages and a shift in employment away from electricity 

generation and toward consumer goods. Note that in long-run equilibrium there is no net effect 

on total employment because wages adjust to bring the labor market into equilibrium. For 

context, the Office of Management and Budget (OMB) currently assumes that the official

unemployment rate may decline to an annual average of 6.3 percent in 2014 and drop further to 

5.4 percent in 2017.

5 The unemployment rate in 2017 is generally considered to be close to “full 

employment.” When an economy is at full employment, any effects on net employment are 

likely to be transitory as workers change jobs (e.g., some workers may need to be retrained or 

require time to search for new jobs) rather than enter or exit longer-term unemployment.

Nonetheless, even to the extent that markets are slow to adjust, DOE anticipates that net labor 

market impacts will be negligible over time due to the small magnitude of the short-term effects 

16-3
presented in Table 16.4.1. The ImSET model projections, assuming no price or wage effects until 

2022, are included in Table 16.4.1. 

16-4
REFERENCES

1. Scott, M., J.M. Roop, R.W. Schultz, D.M. Anderson, and K.A. Cort. The Impact of DOE 

Building Technology Energy Efficiency Programs on U.S. Employment, Income, and 

Investment. Energy Economics, 2008. 30(5): pp. 2283-2301.

2. Scott M.J., O.V. Livingston, P.J. Balducci, J.M. Roop, and R.W. Schultz. ImSET: Impact 

of Sector Energy Technologies Model Description and User’s Guide. 2009. Pacific 

Northwest National Laboratory: Richland, WA. Report No. PNNL-18412.

3. Scott, M.J., D.J. Hostick, and D.B. Belzer. ImBuild: Impact of Building Energy Efficiency 

Programs.1998. Pacific Northwest National Laboratory, Richland, WA. Report No. PNNL11884. 

4. Minnesota IMPLAN Group Inc. IMPLAN Professional: User’s Guide, Analysis Guide, 

Data Guide. 1997. Stillwater, MN.

5. U.S. Office of Management and Budget. Fiscal Year 2015 Mid-Session Review. 2014.

Washington, D.C. 

<http://www.whitehouse.gov/sites/default/files/omb/budget/fy2015/assets/15msr.pdf>

16-5
CHAPTER 17. REGULATORY IMPACT ANALYSIS 

TABLE OF CONTENTS

17.1 INTRODUCTION ......................................................................................................... 17-1

17.2 METHODOLOGY ........................................................................................................ 17-1

17.3 NON-REGULATORY POLICIES................................................................................ 17-2

17.3.1 No New Regulatory Action ............................................................................... 17-2

17.3.2 Customer Rebates .............................................................................................. 17-2

17.3.3 Consumer Tax Credits....................................................................................... 17-4

17.3.4 Voluntary Energy Efficiency Programs............................................................. 17-5

17.3.5 Early Replacement............................................................................................. 17-5

17.4 SUMMARY OF RESULTS FOR NON-REGULATORY ALTERNATIVES ............ 17-5

REFERENCES .......................................................................................................................... 17-7

LIST OF TABLES

Table 17.1.1 Non-Regulatory Alternatives to Standards........................................................... 17-1

Table 17.3.1 Customer Rebate NES and NPV Comparison to TSL 3....................................... 17-4

Table 17.3.2 Tax Credit NES and NPV Comparison to TSL 3 ................................................. 17-4

Table 17.4.1 Cumulative NES of Non-Regulatory Alternatives Compared to the Final Energy 

Conservation Standard....................................................................................... 17-5

Table 17.4.2 Cumulative NPV of Non-Regulatory Alternatives Compared to the Final Energy 

Conservation Standard....................................................................................... 17-6

17-i
CHAPTER 17. REGULATORY IMPACT ANALYSIS

17.1 INTRODUCTION

Under the Process Rule (Procedures for Consideration of New or Revised Energy 

Conservation Standards for Consumer Products, 61 FR 36974 (July 15, 1996), the U.S.

Department of Energy (DOE) is committed to continually explore non-regulatory alternatives to 

standards. DOE will prepare a draft regulatory impact analysis pursuant to E.O. 12866, 

Regulatory Planning and Review, which will be subject to review under the Executive Order by 

the Office of Management and Budget’s Office of Information and Regulatory Affairs. 

58 FR 51735 (Sept. 30, 1993). 

DOE has identified five major alternatives to standards that represent feasible policy 

options to reduce automatic commercial ice-making (ACIM) equipment energy consumption. 

DOE evaluated each alternative’s ability to achieve significant energy savings at a reasonable 

cost, and compared the effectiveness of each one to the effectiveness of the standards in the final

rule.

The non-regulatory means of achieving energy savings that DOE analyzed are listed in 

Table 17.1.1. In support of DOE’s final rule, DOE includes a quantitative analysis of each 

alternative, the methodology for which is discussed briefly in this technical support document 

(TSD).

Table 17.1.1 Non-Regulatory Alternatives to Standards

No new regulatory action

Consumer tax credits

Customer rebates

Voluntary energy efficiency targets

Early replacement

17.2 METHODOLOGY

DOE uses the national impact analysis (NIA) spreadsheet models to calculate the national 

energy savings (NES) and the net present value (NPV) corresponding to each non-regulatory 

alternative. The NIA model is discussed in chapter 10 of this TSD. To compare each alternative 

quantitatively to the energy conservation standards, DOE quantifies the effect of each alternative 

on the purchase and use of energy-efficient ACIM equipment. After quantifying each alternative, 

DOE makes the appropriate revisions to the inputs in the NIA models to estimate energy savings 

compared to the base-case scenario. Key inputs that DOE typically revises in these models 

include the following:

• energy prices and escalation factors,

• implicit market discount rates for trading off purchase price against operating expense 

when choosing product efficiency,

• business purchase prices and operating costs,

• purchase price-versus-efficiency relationships, and

17-1
• product stock data.

The key measures of the impact of each alternative include the following:

• Energy use reflects the cumulative energy use of the product from the effective date 

of the new standard through the year 2047.

• NES represents the cumulative national energy use from the base-case projection 

minus the alternative policy case projection, given in quadrillion British thermal units 

(quads).

• NPV represents the value in 2013$ (discounted to 2014) of net monetary savings 

from products bought during the period from the effective date of the policy (2018) 

through the end of the analysis period (2047).

17.3 NON-REGULATORY POLICIES

17.3.1 No New Regulatory Action

The base-case scenario is the one in which no new regulatory action is taken with regard 

to the energy efficiency of ACIM equipment, as described in the NIA (chapter 10 of this TSD). 

The base-case scenario provides the basis of comparison for all other non-regulatory alternatives. 

By definition, no new regulatory action yields zero NES and an NPV of zero dollars.

17.3.2 Customer Rebates

Customer rebates cover a portion of the difference in incremental product price between 

products meeting baseline efficiency levels and those meeting higher efficiency levels, resulting 

in a higher percentage of consumers purchasing more efficacious models and decreased 

aggregated energy use compared to the base-case scenario. 

DOE surveyed the various rebate programs available in the United States in 2013. 

Typically, local utility companies offer rebates to commercial customers (i.e., business 

customers taking service through a commercial rate code) that replace their existing commercial

ice maker with an energy-efficient machine. Machines qualifying as energy efficient are 

frequently defined as ENERGY STAR® compliant or meeting the Consortium for Energy 

Efficiency’s (CEE’s) Tier 2 or 3. As an example, Eugene Water and Electric Board offers rebates 

starting at $15 per machine for an air-cooled ice making head under 200 lb/day that meets 

ENERGY STAR criteria and increasing up to $271 for an air-cooled remote condensing unit 

greater than or equal to 1,800 lb/day that meets CEE Tier 3.

1 The Pacific Gas and Electric 

Company offers a series of potential rebates depending on the type and harvest capacity of the 

ice maker, ranging from $100 for a 100–300 lb/day machine to $500 for a 1,500 lb/day or larger 

machine.2 The Bonneville Power Administration and 60 participating utilities, with the Portland 

Energy Conservation, Inc. (PECI), operate an EnergySmart Grocer program offering a series of 

rebates ranging from $40 to $300. Grant County Public Utility District also works with PECI to 

offer similar rebates for ACIM equipment.3

In some cases, energy conservation and water conservation are rewarded with rebates. 

The Saving Water Partnership in the Seattle, Washington, metropolitan area combines electricity 

17-2
conservation rebates ranging from $100 to $600 with water conservation rebates of $100 or $300 

(depending on harvest capacity).4

DOE used the amounts of current rebates and compared them to the installed cost

increments at the levels corresponding to trial standard level (TSL) 3. (See chapter 10 of this 

TSD for more information on installed costs and TSL 3.) A $100 rebate equals or exceeds 40 

percent of the incremental installed cost for all small equipment classes based on the difference 

between an efficiency level 1 installed cost and a level corresponding to TSL 3, with percentages 

ranging from 40 percent of an IMH-A-Small-B unit to 116 percent of a SCU-A-Small-C unit

cost differential. For large machines, a $200 rebate is equivalent to 24 percent of the incremental 

cost of an IMH-Large-B2 unit and at the high end is over 200 percent of the incremental cost of

an RCU-Large-B1 or IMH-A-Large-C unit. For the IMH-W-Med-B category, a $100 rebate is 

over 90 percent of the incremental cost and a $200 rebate is over 180 percent of the incremental 

cost.

DOE chose to model a scenario where customers are offered, as rebates, 60 percent of the 

incremental installed cost of the ACIM equipment.

To estimate the market shares of efficiency levels (see chapter 10 of this TSD) that would 

result from such a rebate, DOE examined available information on efficiency choices in 

equipment classes covered by both ENERGY STAR and rebate programs. Information obtained 

by DOE indicates that, on average, 60 percent of total shipments in those equipment classes meet 

or exceed the ENERGY STAR level. For determining the potential impact of rebate programs 

targeting TSL 3, DOE developed a shift scenario. In the shift scenario, market share was shifted 

upward such that the total market share of shipments at or above TSL 3 was 60 percent. To do 

this, DOE calculated the existing market shares of shipments above TSL 3, if any, and the 

remainder of 60 percent minus existing market shares was assigned to the TSL 3 efficiency level. 

Shipments below TSL 3 were distributed across efficiency levels in proportion to base-case 

efficiency distributions. In cases (if any) where the existing cumulative base-case market share at 

or above TSL 3 exceeded 60 percent, the distribution was left at the base-case distribution.

Although the rebate program reduces the total installed cost to the customer, it is financed 

by tax revenues or by utility revenues. Therefore, from a societal perspective, the installed cost at 

any efficiency level does not change with the rebate program; rather, part of the cost is 

transferred from the customer to taxpayers/ratepayers as a whole. Consequently, DOE assumed 

that equipment costs in the rebates scenario were identical to the NIA base-case scenario. 

DOE assumed that rebates would remain in effect for the duration of the analysis period.

Table 17.3.1 presents the NES and NPV values for the 60 percent rebate scenario and compares 

them against the NES and NPV values at TSL 3. NES and NPV are calculated for equipment 

purchased in the 2018–2047 analysis period and include energy savings, operation and 

maintenance costs, and savings extending for the life of equipment purchased in 2047.

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Table 17.3.1 Customer Rebate NES and NPV Comparison to TSL 3

Policy Alternatives

Cumulative Primary 

Energy Savings*

quads

Net Present Value**

billion 2013$

7% Discount Rate 3% Discount Rate

No New Regulatory Action 0 0 0

Consumer Rebates

(60% of price increments) 0.09 0.229 0.501

TSL 3 0.18 0.430 0.942

* Energy savings are in primary energy quads.

** Net present value is the value in the present of a time series of costs and savings.

17.3.3 Consumer Tax Credits

Consumer tax credits are considered a viable non-regulatory market transformation 

program, as shown by the inclusion of Federal consumer tax credits in the Energy Policy Act of 

2005 (EPACT 2005; Pub L. 109-58, 119 Stat 1026 (2005)) for various residential appliances.

From a consumer perspective, the most important difference between rebate and tax credit 

programs is that a rebate can be obtained relatively quickly, whereas receipt of tax credits is 

delayed until income taxes are filed or a tax refund is provided by the Internal Revenue Service.

As with consumer rebates, DOE assumed that consumer tax credits paid 60 percent of the 

incremental product price, but estimated a different response rate. The delay in reimbursement 

makes tax credits less attractive than rebates. Consequently, DOE estimated a response rate that 

is 80 percent of that for rebate programs (60 percent) and, therefore, a corresponding shift of 48 

percent in market shares to or above TSL 3 with no change in total shipments. In cases (if any) 

where the existing cumulative base-case market share at or above TSL 3 exceeded 48 percent, 

the distribution was left at the base-case distribution. DOE estimated NPV and NES values under 

these assumptions, and the results are presented in Table 17.3.2

From a societal perspective, tax credits (like rebates) do not change the installed cost of 

the equipment, but rather transfer a portion of the cost from the consumer to taxpayers as a 

whole. DOE, therefore, assumed that equipment costs in the consumer tax credits scenario were 

identical to the NIA base case.

DOE assumed that tax credits would remain in effect for the duration of the analysis 

period. Table 17.3.2 presents the NES and NPV values for the tax credit scenario and compares 

them against the NES and NPV values at TSL.

Table 17.3.2 Tax Credit NES and NPV Comparison to TSL 3

Policy Alternatives

Cumulative Primary 

Energy Savings*

quads

Net Present Value**

billion 2013$

7% Discount Rate 3% Discount Rate

No New Regulatory Action 0 0 0

Customer Tax Credits 0.07 0.172 0.378

Today's Standards at TSL 3 0.18 0.430 0.942 * Energy savings are in primary energy quads. ** Net present value is the value in the present of a time series of costs and savings. 

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17.3.4 Voluntary Energy Efficiency Programs

While voluntary programs for equipment could be effective, DOE lacks a quantitative 

basis to determine their effectiveness. As previously noted, broader economic and social 

considerations are in play than simple economic return to the equipment purchaser. DOE lacks 

the data necessary to quantitatively project the degree to which such voluntary programs for 

more expensive, higher efficiency equipment like ACIM equipment would modify the market.

17.3.5 Early Replacement

Early replacement refers to the replacement of equipment before the end of its useful life.

The purpose of this policy is to retrofit or replace old, inefficient equipment with high-efficiency 

units. DOE studied the feasibility of a Federal program to promote early replacement of 

appliances and equipment under EPACT 1992. In this study, DOE identified Federal policy 

options for early replacement that include a direct national program, replacement of Federally

owned equipment, promotion through equipment manufacturers, customer incentives, incentives 

to utilities, market behavior research, and building regulations. 

While cost-effective opportunities to install more-efficient units exist, DOE determined 

that a Federal early replacement program is not economically justified because the market for 

automatic commercial ice makers is relatively small, especially for Federally owned equipment, 

and distributed across a broad set of customers; thus, the savings would not be likely to be 

significant. 

17.4 SUMMARY OF RESULTS FOR NON-REGULATORY ALTERNATIVES

Table 17.4.1 and Table 17.4.2 show the NES and NPV for the non-regulatory alternatives 

analyzed. The case in which no regulatory action is taken constitutes the base-case scenario. 

Because this is the base-case scenario, NES and NPV are zero by definition. For comparison, the 

tables include the results of the NES and NPV at TSL 3 associated with the energy conservation 

standard.

As shown in Table 17.4.1 and Table 17.4.2, none of the policy alternatives DOE 

examined would achieve the amount of energy or monetary savings that could be realized under 

the final rule. In addition, implementing either tax credits or customer rebates would incur initial 

and/or administrative costs not considered in this analysis.

Table 17.4.1 Cumulative NES of Non-Regulatory Alternatives Compared to the Final 

Energy Conservation Standard

Policy Alternatives Cumulative NES

quads

No new regulatory action 0

Consumer tax credits 0.07

Customer rebates 0.09

Voluntary energy efficiency targets 0

Early replacement 0

Final standards (TSL 3) 0.18

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Table 17.4.2 Cumulative NPV of Non-Regulatory Alternatives Compared to the Final 

Energy Conservation Standard

Policy Alternatives

Cumulative Net Present Value

billion 2013$

7% Discount 3% Discount

No new regulatory action 0 0

Consumer tax credits 0.172 0.378

Customer rebates 0.229 0.501

Voluntary energy efficiency targets 0 0

Early replacement 0 0

Final standards (TSL 3) 0.430 0.942

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REFERENCES

1. Eugene Water and Electric Board. Commercial Food Service Equipment Catalog. 2013.

Eugene, OR. <http://eweb.org/public/documents/energy/FoodServicesCatalog.pdf>

2. Pacific Gas and Electric Company. Food Service Rebate Catalog. 2013. (Last accessed 

February 12, 2013.) 

<www.pge.com/en/mybusiness/energysavingsrebates/rebatesincentives/index.page>

3. Portland Energy Conservation, Inc. Energy Smart. (Last accessed February 12, 2013.) 

<www.energysmartonline.org/rebates/index.html> 

4. Saving Water Partnership. Rebates. Seattle, WA. (Last accessed February 13, 2013.) 

<www.savingwater.org/rebates.htm>

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